Robotics 23
★ Q-learning-based Model-free Safety Filter
Ensuring safety via safety filters in real-world robotics presents
significant challenges, particularly when the system dynamics is complex or
unavailable. To handle this issue, learning-based safety filters recently
gained popularity, which can be classified as model-based and model-free
methods. Existing model-based approaches requires various assumptions on system
model (e.g., control-affine), which limits their application in complex
systems, and existing model-free approaches need substantial modifications to
standard RL algorithms and lack versatility. This paper proposes a simple,
plugin-and-play, and effective model-free safety filter learning framework. We
introduce a novel reward formulation and use Q-learning to learn Q-value
functions to safeguard arbitrary task specific nominal policies via filtering
out their potentially unsafe actions. The threshold used in the filtering
process is supported by our theoretical analysis. Due to its model-free nature
and simplicity, our framework can be seamlessly integrated with various RL
algorithms. We validate the proposed approach through simulations on double
integrator and Dubin's car systems and demonstrate its effectiveness in
real-world experiments with a soft robotic limb.
comment: *Denotes equal contribution
☆ URDF+: An Enhanced URDF for Robots with Kinematic Loops
Designs incorporating kinematic loops are becoming increasingly prevalent in
the robotics community. Despite the existence of dynamics algorithms to deal
with the effects of such loops, many modern simulators rely on dynamics
libraries that require robots to be represented as kinematic trees. This
requirement is reflected in the de facto standard format for describing robots,
the Universal Robot Description Format (URDF), which does not support kinematic
loops resulting in closed chains. This paper introduces an enhanced URDF,
termed URDF+, which addresses this key shortcoming of URDF while retaining the
intuitive design philosophy and low barrier to entry that the robotics
community values. The URDF+ keeps the elements used by URDF to describe open
chains and incorporates new elements to encode loop joints. We also offer an
accompanying parser that processes the system models coming from URDF+ so that
they can be used with recursive rigid-body dynamics algorithms for closed-chain
systems that group bodies into local, decoupled loops. This parsing process is
fully automated, ensuring optimal grouping of constrained bodies without
requiring manual specification from the user. We aim to advance the robotics
community towards this elegant solution by developing efficient and easy-to-use
software tools.
comment: 8 pages, 5 figures, 2024 IEEE-RAS International Conference on
Humanoid Robots
☆ A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses
Trajectory prediction is essential for the safety and efficiency of planning
in autonomous vehicles. However, current models often fail to fully capture
complex traffic rules and the complete range of potential vehicle movements.
Addressing these limitations, this study introduces three novel loss functions:
Offroad Loss, Direction Consistency Error, and Diversity Loss. These functions
are designed to keep predicted paths within driving area boundaries, aligned
with traffic directions, and cover a wider variety of plausible driving
scenarios. As all prediction modes should adhere to road rules and conditions,
this work overcomes the shortcomings of traditional "winner takes all" training
methods by applying the loss functions to all prediction modes. These loss
functions not only improve model training but can also serve as metrics for
evaluating the realism and diversity of trajectory predictions. Extensive
validation on the nuScenes and Argoverse 2 datasets with leading baseline
models demonstrates that our approach not only maintains accuracy but
significantly improves safety and robustness, reducing offroad errors on
average by 47% on original and by 37% on attacked scenes. This work sets a new
benchmark for trajectory prediction in autonomous driving, offering substantial
improvements in navigating complex environments. Our code is available at
https://github.com/vita-epfl/stay-on-track .
comment: Preprint, 7 pages, 4 figures and 2 tables
☆ Improving generalization of robot locomotion policies via Sharpness-Aware Reinforcement Learning
Reinforcement learning often requires extensive training data.
Simulation-to-real transfer offers a promising approach to address this
challenge in robotics. While differentiable simulators offer improved sample
efficiency through exact gradients, they can be unstable in contact-rich
environments and may lead to poor generalization. This paper introduces a novel
approach integrating sharpness-aware optimization into gradient-based
reinforcement learning algorithms. Our simulation results demonstrate that our
method, tested on contact-rich environments, significantly enhances policy
robustness to environmental variations and action perturbations while
maintaining the sample efficiency of first-order methods. Specifically, our
approach improves action noise tolerance compared to standard first-order
methods and achieves generalization comparable to zeroth-order methods. This
improvement stems from finding flatter minima in the loss landscape, associated
with better generalization. Our work offers a promising solution to balance
efficient learning and robust sim-to-real transfer in robotics, potentially
bridging the gap between simulation and real-world performance.
comment: 9 pages, 6 figures
☆ MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estimation by Planar-Parallax Geometry in Automotive Applications WACV 25
Self-supervised monocular depth estimation (MDE) has gained popularity for
obtaining depth predictions directly from videos. However, these methods often
produce scale invariant results, unless additional training signals are
provided. Addressing this challenge, we introduce a novel self-supervised
metric-scaled MDE model that requires only monocular video data and the
camera's mounting position, both of which are readily available in modern
vehicles. Our approach leverages planar-parallax geometry to reconstruct scene
structure. The full pipeline consists of three main networks, a multi-frame
network, a singleframe network, and a pose network. The multi-frame network
processes sequential frames to estimate the structure of the static scene using
planar-parallax geometry and the camera mounting position. Based on this
reconstruction, it acts as a teacher, distilling knowledge such as scale
information, masked drivable area, metric-scale depth for the static scene, and
dynamic object mask to the singleframe network. It also aids the pose network
in predicting a metric-scaled relative pose between two subsequent images. Our
method achieved state-of-the-art results for the driving benchmark KITTI for
metric-scaled depth prediction. Notably, it is one of the first methods to
produce self-supervised metric-scaled depth prediction for the challenging
Cityscapes dataset, demonstrating its effectiveness and versatility.
comment: Accepted at WACV 25, project page: https://mono-pp.github.io/
☆ CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
Qixiu Li, Yaobo Liang, Zeyu Wang, Lin Luo, Xi Chen, Mozheng Liao, Fangyun Wei, Yu Deng, Sicheng Xu, Yizhong Zhang, Xiaofan Wang, Bei Liu, Jianlong Fu, Jianmin Bao, Dong Chen, Yuanchun Shi, Jiaolong Yang, Baining Guo
The advancement of large Vision-Language-Action (VLA) models has
significantly improved robotic manipulation in terms of language-guided task
execution and generalization to unseen scenarios. While existing VLAs adapted
from pretrained large Vision-Language-Models (VLM) have demonstrated promising
generalizability, their task performance is still unsatisfactory as indicated
by the low tasks success rates in different environments. In this paper, we
present a new advanced VLA architecture derived from VLM. Unlike previous works
that directly repurpose VLM for action prediction by simple action
quantization, we propose a omponentized VLA architecture that has a specialized
action module conditioned on VLM output. We systematically study the design of
the action module and demonstrates the strong performance enhancement with
diffusion action transformers for action sequence modeling, as well as their
favorable scaling behaviors. We also conduct comprehensive experiments and
ablation studies to evaluate the efficacy of our models with varied designs.
The evaluation on 5 robot embodiments in simulation and real work shows that
our model not only significantly surpasses existing VLAs in task performance
and but also exhibits remarkable adaptation to new robots and generalization to
unseen objects and backgrounds. It exceeds the average success rates of OpenVLA
which has similar model size (7B) with ours by over 35% in simulated evaluation
and 55% in real robot experiments. It also outperforms the large RT-2-X model
(55B) by 18% absolute success rates in simulation. Code and models can be found
on our project page (https://cogact.github.io/).
comment: Project Webpage: https://cogact.github.io/
☆ The ATTUNE model for Artificial Trust Towards Human Operators
This paper presents a novel method to quantify Trust in HRI. It proposes an
HRI framework for estimating the Robot Trust towards the Human in the context
of a narrow and specified task. The framework produces a real-time estimation
of an AI agent's Artificial Trust towards a Human partner interacting with a
mobile teleoperation robot. The approach for the framework is based on
principles drawn from Theory of Mind, including information about the human
state, action, and intent. The framework creates the ATTUNE model for
Artificial Trust Towards Human Operators. The model uses metrics on the
operator's state of attention, navigational intent, actions, and performance to
quantify the Trust towards them. The model is tested on a pre-existing dataset
that includes recordings (ROSbags) of a human trial in a simulated disaster
response scenario. The performance of ATTUNE is evaluated through a qualitative
and quantitative analysis. The results of the analyses provide insight into the
next stages of the research and help refine the proposed approach.
comment: Published in IEEE SMC 2024
☆ RoadGen: Generating Road Scenarios for Autonomous Vehicle Testing
With the rapid development of autonomous vehicles, there is an increasing
demand for scenario-based testing to simulate diverse driving scenarios.
However, as the base of any driving scenarios, road scenarios (e.g., road
topology and geometry) have received little attention by the literature.
Despite several advances, they either generate basic road components without a
complete road network, or generate a complete road network but with simple road
components. The resulting road scenarios lack diversity in both topology and
geometry. To address this problem, we propose RoadGen to systematically
generate diverse road scenarios. The key idea is to connect eight types of
parameterized road components to form road scenarios with high diversity in
topology and geometry. Our evaluation has demonstrated the effectiveness and
usefulness of RoadGen in generating diverse road scenarios for simulation.
comment: 7 pages
☆ AdvFuzz: Finding More Violations Caused by the EGO Vehicle in Simulation Testing by Adversarial NPC Vehicles
Recently, there has been a significant escalation in both academic and
industrial commitment towards the development of autonomous driving systems
(ADSs). A number of simulation testing approaches have been proposed to
generate diverse driving scenarios for ADS testing. However, scenarios
generated by these previous approaches are static and lack interactions between
the EGO vehicle and the NPC vehicles, resulting in a large amount of time on
average to find violation scenarios. Besides, a large number of the violations
they found are caused by aggressive behaviors of NPC vehicles, revealing none
bugs of ADS.
In this work, we propose the concept of adversarial NPC vehicles and
introduce AdvFuzz, a novel simulation testing approach, to generate adversarial
scenarios on main lanes (e.g., urban roads and highways). AdvFuzz allows NPC
vehicles to dynamically interact with the EGO vehicle and regulates the
behaviors of NPC vehicles, finding more violation scenarios caused by the EGO
vehicle more quickly. We compare AdvFuzz with a random approach and three
state-of-the-art scenario-based testing approaches. Our experiments demonstrate
that AdvFuzz can generate 198.34% more violation scenarios compared to the
other four approaches in 12 hours and increase the proportion of violations
caused by the EGO vehicle to 87.04%, which is more than 7 times that of other
approaches. Additionally, AdvFuzz is at least 92.21% faster in finding one
violation caused by the EGO vehicle than that of the other approaches.
comment: 21 pages
☆ ReconDreamer: Crafting World Models for Driving Scene Reconstruction via Online Restoration
Chaojun Ni, Guosheng Zhao, Xiaofeng Wang, Zheng Zhu, Wenkang Qin, Guan Huang, Chen Liu, Yuyin Chen, Yida Wang, Xueyang Zhang, Yifei Zhan, Kun Zhan, Peng Jia, Xianpeng Lang, Xingang Wang, Wenjun Mei
Closed-loop simulation is crucial for end-to-end autonomous driving. Existing
sensor simulation methods (e.g., NeRF and 3DGS) reconstruct driving scenes
based on conditions that closely mirror training data distributions. However,
these methods struggle with rendering novel trajectories, such as lane changes.
Recent works have demonstrated that integrating world model knowledge
alleviates these issues. Despite their efficiency, these approaches still
encounter difficulties in the accurate representation of more complex
maneuvers, with multi-lane shifts being a notable example. Therefore, we
introduce ReconDreamer, which enhances driving scene reconstruction through
incremental integration of world model knowledge. Specifically, DriveRestorer
is proposed to mitigate artifacts via online restoration. This is complemented
by a progressive data update strategy designed to ensure high-quality rendering
for more complex maneuvers. To the best of our knowledge, ReconDreamer is the
first method to effectively render in large maneuvers. Experimental results
demonstrate that ReconDreamer outperforms Street Gaussians in the NTA-IoU,
NTL-IoU, and FID, with relative improvements by 24.87%, 6.72%, and 29.97%.
Furthermore, ReconDreamer surpasses DriveDreamer4D with PVG during large
maneuver rendering, as verified by a relative improvement of 195.87% in the
NTA-IoU metric and a comprehensive user study.
comment: Project Page: https://recondreamer.github.io
☆ A Unified Interaction Control Framework for Safe Robotic Ultrasound Scanning with Human-Intention-Aware Compliance
Xiangjie Yan, Shaqi Luo, Yongpeng Jiang, Mingrui Yu, Chen Chen, Senqiang Zhu, Gao Huang, Shiji Song, Xiang Li
The ultrasound scanning robot operates in environments where frequent
human-robot interactions occur. Most existing control methods for ultrasound
scanning address only one specific interaction situation or implement hard
switches between controllers for different situations, which compromises both
safety and efficiency. In this paper, we propose a unified interaction control
framework for ultrasound scanning robots capable of handling all common
interactions, distinguishing both human-intended and unintended types, and
adapting with appropriate compliance. Specifically, the robot suspends or
modulates its ongoing main task if the interaction is intended, e.g., when the
doctor grasps the robot to lead the end effector actively. Furthermore, it can
identify unintended interactions and avoid potential collision in the null
space beforehand. Even if that collision has happened, it can become compliant
with the collision in the null space and try to reduce its impact on the main
task (where the scan is ongoing) kinematically and dynamically. The multiple
situations are integrated into a unified controller with a smooth transition to
deal with the interactions by exhibiting human-intention-aware compliance.
Experimental results validate the framework's ability to cope with all common
interactions including intended intervention and unintended collision in a
collaborative carotid artery ultrasound scanning task.
☆ A Local Information Aggregation based Multi-Agent Reinforcement Learning for Robot Swarm Dynamic Task Allocation
In this paper, we explore how to optimize task allocation for robot swarms in
dynamic environments, emphasizing the necessity of formulating robust,
flexible, and scalable strategies for robot cooperation. We introduce a novel
framework using a decentralized partially observable Markov decision process
(Dec_POMDP), specifically designed for distributed robot swarm networks. At the
core of our methodology is the Local Information Aggregation Multi-Agent Deep
Deterministic Policy Gradient (LIA_MADDPG) algorithm, which merges centralized
training with distributed execution (CTDE). During the centralized training
phase, a local information aggregation (LIA) module is meticulously designed to
gather critical data from neighboring robots, enhancing decision-making
efficiency. In the distributed execution phase, a strategy improvement method
is proposed to dynamically adjust task allocation based on changing and
partially observable environmental conditions. Our empirical evaluations show
that the LIA module can be seamlessly integrated into various CTDE-based MARL
methods, significantly enhancing their performance. Additionally, by comparing
LIA_MADDPG with six conventional reinforcement learning algorithms and a
heuristic algorithm, we demonstrate its superior scalability, rapid adaptation
to environmental changes, and ability to maintain both stability and
convergence speed. These results underscore LIA_MADDPG's outstanding
performance and its potential to significantly improve dynamic task allocation
in robot swarms through enhanced local collaboration and adaptive strategy
execution.
☆ SANGO: Socially Aware Navigation through Grouped Obstacles
This paper introduces SANGO (Socially Aware Navigation through Grouped
Obstacles), a novel method that ensures socially appropriate behavior by
dynamically grouping obstacles and adhering to social norms. Using deep
reinforcement learning, SANGO trains agents to navigate complex environments
leveraging the DBSCAN algorithm for obstacle clustering and Proximal Policy
Optimization (PPO) for path planning. The proposed approach improves safety and
social compliance by maintaining appropriate distances and reducing collision
rates. Extensive experiments conducted in custom simulation environments
demonstrate SANGO's superior performance in significantly reducing discomfort
(by up to 83.5%), reducing collision rates (by up to 29.4%) and achieving
higher successful navigation in dynamic and crowded scenarios. These findings
highlight the potential of SANGO for real-world applications, paving the way
for advanced socially adept robotic navigation systems.
comment: Indian Control Conference 2024 (ICC-10)
☆ Loop Shaping of Hybrid Motion Control with Contact Transition
A standard (stiff) motion control with output displacement feedback cannot
handle unforeseen contact with environment without penetrating into soft, i.e.
viscoelastic, materials or even damaging brittle or fragile materials. Robotics
and mechatronics with tactile and haptic capabilities, and medical assistance
systems in particular, place special demands on the advanced motion control
systems that should enable safe and harmless contact transitions. This paper
demonstrates how the fundamental principles of loop shaping can easily be used
to handle the sufficiently stiff motion control with a sensor-free dynamic
extension to reconfigure at contact with environment. Hybrid control scheme is
proposed. Remarkable feature of the developed approach is that no measurement
of the contact force is required and the input signal and measured output
displacement are the only quantities used for control design and operation.
Experimental scenarios for 1DOF actuator are shown where the moving tool comes
into contact with grape fruits that are soft and penetrable at the same time.
comment: 6 pages, 8 figures
☆ Robust Bayesian Scene Reconstruction by Leveraging Retrieval-Augmented Priors
Constructing 3D representations of object geometry is critical for many
downstream manipulation tasks. These representations must be built from
potentially noisy partial observations. In this work we focus on the problem of
reconstructing a multi-object scene from a single RGBD image. Current deep
learning approaches to this problem can be brittle to noisy real world
observations and out-of-distribution objects. Other approaches that do not rely
on training data cannot accurately infer the backside of objects. We propose
BRRP, a reconstruction method that can leverage preexisting mesh datasets to
build an informative prior during robust probabilistic reconstruction. In order
to make our method more efficient, we introduce the concept of
retrieval-augmented prior, where we retrieve relevant components of our prior
distribution during inference. Our method produces a distribution over object
shape that can be used for reconstruction or measuring uncertainty. We evaluate
our method in both procedurally generated scenes and in real world scenes. We
show our method is more robust than a deep learning approach while being more
accurate than a method with an uninformative prior.
♻ ☆ Safe, Out-of-Distribution-Adaptive MPC with Conformalized Neural Network Ensembles
We present SODA-MPC, a Safe, Out-of-Distribution-Adaptive Model Predictive
Control algorithm, which uses an ensemble of learned models for prediction,
with a runtime monitor to flag unreliable out-of-distribution (OOD)
predictions. When an OOD situation is detected, SODA-MPC triggers a safe
fallback control strategy based on reachability, yielding a control framework
that achieves the high performance of learning-based models while preserving
the safety of reachability-based control. We demonstrate the method in the
context of an autonomous vehicle, driving among dynamic pedestrians, where
SODA-MPC uses a neural network ensemble for pedestrian prediction. We calibrate
the OOD signal using conformal prediction to derive an OOD detector with
probabilistic guarantees on the false-positive rate, given a user-specified
confidence level. During in-distribution operation, the MPC controller avoids
collisions with a pedestrian based on the trajectory predicted by the mean of
the ensemble. When OOD conditions are detected, the MPC switches to a
reachability-based controller to avoid collisions with the reachable set of the
pedestrian assuming a maximum pedestrian speed, to guarantee safety under the
worst-case actions of the pedestrian. We verify SODA-MPC in extensive
autonomous driving simulations in a pedestrian-crossing scenario. Our model
ensemble is trained and calibrated with real pedestrian data, showing that our
OOD detector obtains the desired accuracy rate within a theoretically-predicted
range. We empirically show improved safety and improved task completion
compared with two state-of-the-art MPC methods that also use conformal
prediction, but without OOD adaptation. Further, we demonstrate the
effectiveness of our method with the large-scale multi-agent predictor
Trajectron++, using large-scale traffic data from the nuScenes dataset for
training and calibration.
♻ ☆ SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecasts
Royden Wagner, Ömer Sahin Tas, Marlon Steiner, Fabian Konstantinidis, Hendrik Königshof, Marvin Klemp, Carlos Fernandez, Christoph Stiller
Self-driving vehicles rely on multimodal motion forecasts to effectively
interact with their environment and plan safe maneuvers. We introduce
SceneMotion, an attention-based model for forecasting scene-wide motion modes
of multiple traffic agents. Our model transforms local agent-centric embeddings
into scene-wide forecasts using a novel latent context module. This module
learns a scene-wide latent space from multiple agent-centric embeddings,
enabling joint forecasting and interaction modeling. The competitive
performance in the Waymo Open Interaction Prediction Challenge demonstrates the
effectiveness of our approach. Moreover, we cluster future waypoints in time
and space to quantify the interaction between agents. We merge all modes and
analyze each mode independently to determine which clusters are resolved
through interaction or result in conflict. Our implementation is available at:
https://github.com/kit-mrt/future-motion
comment: ITSC'24; updated table VI
♻ ☆ Learning Local Control Barrier Functions for Hybrid Systems
Hybrid dynamical systems are ubiquitous as practical robotic applications
often involve both continuous states and discrete switchings. Safety is a
primary concern for hybrid robotic systems. Existing safety-critical control
approaches for hybrid systems are either computationally inefficient,
detrimental to system performance, or limited to small-scale systems. To amend
these drawbacks, in this paper, we propose a learning-enabled approach to
construct local Control Barrier Functions (CBFs) to guarantee the safety of a
wide class of nonlinear hybrid dynamical systems. The end result is a safe
neural CBF-based switching controller. Our approach is computationally
efficient, minimally invasive to any reference controller, and applicable to
large-scale systems. We empirically evaluate our framework and demonstrate its
efficacy and flexibility through two robotic examples including a
high-dimensional autonomous racing case, against other CBF-based approaches and
model predictive control.
♻ ☆ Embedded Hierarchical MPC for Autonomous Navigation
To efficiently deploy robotic systems in society, mobile robots need to
autonomously and safely move through complex environments. Nonlinear model
predictive control (MPC) methods provide a natural way to find a dynamically
feasible trajectory through the environment without colliding with nearby
obstacles. However, the limited computation power available on typical embedded
robotic systems, such as quadrotors, poses a challenge to running MPC in
real-time, including its most expensive tasks: constraints generation and
optimization. To address this problem, we propose a novel hierarchical MPC
scheme that consists of a planning and a tracking layer. The planner constructs
a trajectory with a long prediction horizon at a slow rate, while the tracker
ensures trajectory tracking at a relatively fast rate. We prove that the
proposed framework avoids collisions and is recursively feasible. Furthermore,
we demonstrate its effectiveness in simulations and lab experiments with a
quadrotor that needs to reach a goal position in a complex static environment.
The code is efficiently implemented on the quadrotor's embedded computer to
ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC
formulation, this allows us to increase the planning horizon by a factor of 5,
which results in significantly better performance.
comment: 19 pages, 15 figures (excluding biography entries)
♻ ☆ Rule-Based Lloyd Algorithm for Multi-Robot Motion Planning and Control with Safety and Convergence Guarantees
This paper presents a distributed rule-based Lloyd algorithm (RBL) for
multi-robot motion planning and control. The main limitations of the basic
Loyd-based algorithm (LB) concern deadlock issues and the failure to address
dynamic constraints effectively. Our contribution is twofold. First, we show
how RBL is able to provide safety and convergence to the goal region without
relying on communication between robots, nor synchronization between the
robots. We considered different dynamic constraints with control inputs
saturation. Second, we show that the Lloyd-based algorithm (without rules) can
be successfully used as a safety layer for learning-based approaches, leading
to non-negligible benefits. We further prove the soundness, reliability, and
scalability of RBL through extensive simulations, comparisons with the state of
the art, and experimental validations on small-scale car-like robots,
unicycle-like robots, omnidirectional robots, and aerial robots on the field.
♻ ☆ Barrier-Enhanced Parallel Homotopic Trajectory Optimization for Safety-Critical Autonomous Driving
Enforcing safety while preventing overly conservative behaviors is essential
for autonomous vehicles to achieve high task performance. In this paper, we
propose a barrier-enhanced parallel homotopic trajectory optimization (BPHTO)
approach with the over-relaxed alternating direction method of multipliers
(ADMM) for real-time integrated decision-making and planning. To facilitate
safety interactions between the ego vehicle (EV) and surrounding vehicles, a
spatiotemporal safety module exhibiting bi-convexity is developed on the basis
of barrier function. Varying barrier coefficients are adopted for different
time steps in a planning horizon to account for the motion uncertainties of
surrounding HVs and mitigate conservative behaviors. Additionally, we exploit
the discrete characteristics of driving maneuvers to initialize nominal
behavior-oriented free-end homotopic trajectories based on reachability
analysis, and each trajectory is locally constrained to a specific driving
maneuver while sharing the same task objectives. By leveraging the bi-convexity
of the safety module and the kinematics of the EV, we formulate the BPHTO as a
bi-convex optimization problem. Then constraint transcription and the
over-relaxed ADMM are employed to streamline the optimization process, such
that multiple trajectories are generated in real time with feasibility
guarantees. Through a series of experiments, the proposed development
demonstrates improved task accuracy, stability, and consistency in various
traffic scenarios using synthetic and real-world traffic datasets.
comment: 17 pages, 10 figures, accepted for publication in IEEE Transactions
on Intelligent Transportation Systems
♻ ☆ FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots
The reinforcement learning algorithms have often been applied to social
robots. However, most reinforcement learning algorithms were not optimized for
the use of social robots, and consequently they may bore users. We proposed a
new reinforcement learning method specialized for the social robot, the
FRAC-Q-learning, that can avoid user boredom. The proposed algorithm consists
of a forgetting process in addition to randomizing and categorizing processes.
This study evaluated interest and boredom hardness scores of the
FRAC-Q-learning by a comparison with the traditional Q-learning. The
FRAC-Q-learning showed significantly higher trend of interest score, and
indicated significantly harder to bore users compared to the traditional
Q-learning. Therefore, the FRAC-Q-learning can contribute to develop a social
robot that will not bore users. The proposed algorithm has a potential to apply
for Web-based communication and educational systems. This paper presents the
entire process, detailed implementation and a detailed evaluation method of the
of the FRAC-Q-learning for the first time.
♻ ★ SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation
Xu Liu, Jiuzhou Lei, Ankit Prabhu, Yuezhan Tao, Igor Spasojevic, Pratik Chaudhari, Nikolay Atanasov, Vijay Kumar
This paper develops a real-time decentralized metric-semantic Simultaneous
Localization and Mapping (SLAM) algorithm framework that enables a
heterogeneous robot team to collaboratively construct object-based
metric-semantic maps of 3D environments featuring indoor, urban, and forests
without relying on GPS. The framework integrates a data-driven front-end for
instance segmentation from either RGBD cameras or LiDARs and a custom back-end
for optimizing robot trajectories and object landmarks in the map. To allow
multiple robots to merge their information, we design semantics-driven place
recognition algorithms that leverage the informativeness and viewpoint
invariance of the object-level metric-semantic map for inter-robot loop closure
detection. A communication module is designed to track each robot's
observations and those of other robots whenever communication links are
available. Our framework enables real-time decentralized operations onboard
robots, allowing them to opportunistically leverage communication. We integrate
the proposed framework with the autonomous navigation and exploration systems
of three types of aerial and ground robots, conducting extensive experiments in
a variety of indoor and outdoor environments. These experiments demonstrate
accuracy in inter-robot localization and object mapping, along with its
moderate demands on computation, storage, and communication resources. The
framework is open-sourced and available as a modular stack for object-level
metric-semantic SLAM, suitable for both single-agent and multi-robot scenarios.
The project website and code can be found at
https://xurobotics.github.io/slideslam/ and
https://github.com/XuRobotics/SLIDE_SLAM, respectively.
comment: Xu Liu, Jiuzhou Lei, and Ankit Prabhu contributed equally to this
work. This is a preliminary release and is subject to improvement