6 accepted papers
In the classical reach-avoid problem, autonomous mobile robots are tasked to reach a goal while avoiding obstacles. However, it is difficult to provide guarantees on the robot's performance when the obstacles form a narrow gap and the robot is a black-box (i.e. the dynamics are not known analyticall
Model-free diffusion planners have shown great promise for robot motion planning, but practical robotic systems often require combining them with model-based optimization modules to enforce constraints, such as safety. Na\"ively integrating these modules presents compatibility challenges when diffus…
Imitation learning (IL) has shown great success in learning complex robot manipulation tasks. However, there remains a need for practical safety methods to justify widespread deployment. In particular, it is important to certify that a system obeys hard constraints on unsafe behavior in settings whe
Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined to execute the task at the same speed as shown in demonstration data. This limits the task throughput of a robotic…
Autonomous mobile robots must maintain safety, but should not sacrifice performance, leading to the classical reach-avoid problem: find a trajectory that is guaranteed to reach a goal and avoid obstacles. This paper addresses the near danger case, also known as a narrow gap, where the agent starts n…
In this letter, we consider vision-based control tasks of which the desired goals are given as target images. The problems are often addressed by an autonomous agent which optimizes a trajectory to minimize a manually designed cost function. However, it is challenging to design a suitable cost funct