Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy

• URL: http://arxiv.org/abs/2309.01267v2
• Date: Wed, 1 Nov 2023 19:01:10 GMT
• Title: Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy
• Authors: Haimin Hu, Zixu Zhang, Kensuke Nakamura, Andrea Bajcsy, Jaime F. Fisac
• Abstract summary: Existing safety methods often neglect the robot's ability to learn and adapt at runtime, leading to overly conservative behavior. This paper proposes a new closed-loop paradigm for synthesizing safe control policies that explicitly account for the robot's evolving uncertainty.
• Score: 7.915956857741506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot's ability to learn and adapt at runtime, leading to overly conservative behavior. This paper proposes a new closed-loop paradigm for synthesizing safe control policies that explicitly account for the robot's evolving uncertainty and its ability to quickly respond to future scenarios as they arise, by jointly considering the physical dynamics and the robot's learning algorithm. We leverage adversarial reinforcement learning for tractable safety analysis under high-dimensional learning dynamics and demonstrate our framework's ability to work with both Bayesian belief propagation and implicit learning through large pre-trained neural trajectory predictors.

Related papers

• Robot Navigation with Entity-Based Collision Avoidance using Deep Reinforcement Learning [0.0]
  We present a novel methodology that enhances the robot's interaction with different types of agents and obstacles. This approach uses information about the entity types, improving collision avoidance and ensuring safer navigation. We introduce a new reward function that penalizes the robot for collisions with different entities such as adults, bicyclists, children, and static obstacles.
  arXiv  Detail & Related papers  (2024-08-26T11:16:03Z)
• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• ABNet: Attention BarrierNet for Safe and Scalable Robot Learning [58.4951884593569]
  Barrier-based method is one of the dominant approaches for safe robot learning. We propose Attention BarrierNet (ABNet) that is scalable to build larger foundational safe models in an incremental manner. We demonstrate the strength of ABNet in 2D robot obstacle avoidance, safe robot manipulation, and vision-based end-to-end autonomous driving.
  arXiv  Detail & Related papers  (2024-06-18T19:37:44Z)
• Safe Reinforcement Learning on the Constraint Manifold: Theory and Applications [21.98309272057848]
  We show how we can impose complex safety constraints on learning-based robotics systems in a principled manner. Our approach is based on the concept of the Constraint Manifold, representing the set of safe robot configurations. We demonstrate the method's effectiveness in a real-world Robot Air Hockey task.
  arXiv  Detail & Related papers  (2024-04-13T20:55:15Z)
• Learning Vision-based Pursuit-Evasion Robot Policies [54.52536214251999]
  We develop a fully-observable robot policy that generates supervision for a partially-observable one. We deploy our policy on a physical quadruped robot with an RGB-D camera on pursuit-evasion interactions in the wild.
  arXiv  Detail & Related papers  (2023-08-30T17:59:05Z)
• Bridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics [11.946807588018595]
  This paper presents a unified model-based reinforcement learning framework that bridges active exploration and uncertainty-aware deployment. The two opposing tasks of exploration and deployment are optimized through state-of-the-art sampling-based MPC. We conduct experiments on both autonomous vehicles and wheeled robots, showing promising results for both exploration and deployment.
  arXiv  Detail & Related papers  (2023-05-20T17:20:12Z)
• Safe reinforcement learning of dynamic high-dimensional robotic tasks: navigation, manipulation, interaction [31.553783147007177]
  In reinforcement learning, safety is even more fundamental for exploring an environment without causing any damage. This paper introduces a new formulation of safe exploration for reinforcement learning of various robotic tasks. Our approach applies to a wide class of robotic platforms and enforces safety even under complex collision constraints learned from data.
  arXiv  Detail & Related papers  (2022-09-27T11:23:49Z)
• Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning [121.9708998627352]
  Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off. This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
  arXiv  Detail & Related papers  (2022-04-15T08:12:15Z)
• Dual-Arm Adversarial Robot Learning [0.6091702876917281]
  We propose dual-arm settings as platforms for robot learning. We will discuss the potential benefits of this setup as well as the challenges and research directions that can be pursued.
  arXiv  Detail & Related papers  (2021-10-15T12:51:57Z)
• Adversarial Training is Not Ready for Robot Learning [55.493354071227174]
  Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations. We show theoretically and experimentally that neural controllers obtained via adversarial training are subjected to three types of defects. Our results suggest that adversarial training is not yet ready for robot learning.
  arXiv  Detail & Related papers  (2021-03-15T07:51:31Z)
• Neural Dynamic Policies for End-to-End Sensorimotor Learning [51.24542903398335]
  The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space. NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks.
  arXiv  Detail & Related papers  (2020-12-04T18:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.