13 accepted papers
Autonomous navigation that relies on precise metric maps is inherently fragile to environmental changes and mapping inaccuracies. These discrepancies often lead to failures in localization and path planning, as the robot's internal representation of the world no longer matches reality. We propose an…
Offline reinforcement learning (RL) aims to learn the optimal policy from a fixed dataset generated by behavior policies without additional environment interactions. One common challenge that arises in this setting is the out-of-distribution (OOD) error, which occurs when the policy leaves the train…
Recent advances in deep reinforcement learning (RL) have achieved strong results on high-dimensional control tasks, but applying RL to reachability problems raises a fundamental mismatch: reachability seeks to maximize the set of states from which a system remains safe indefinitely, while RL optimiz…
Control policies that can achieve high task performance and satisfy safety constraints are desirable for any system, including multi-agent systems (MAS). One promising technique for ensuring the safety of MAS is distributed control barrier functions (CBF). However, it is difficult to design distribu…
We address the problem of safe policy learning in multi-agent safety-critical autonomous systems. In such systems, it is necessary for each agent to meet the safety requirements at all times while also cooperating with other agents to accomplish the task. Toward this end, we propose a safe Hierarchi…
Tasks for multi-robot systems often require the robots to collaborate and complete a team goal while maintaining safety. This problem is usually formalized as a Constrained Markov decision process (CMDP), which targets minimizing a global cost and bringing the mean of constraint violation below a us…
We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals. This paper addresses the problem…
We explore a method for grasping novel target objects through push-grasping synergy in a cluttered environment without using object detection and segmentation algorithms. The target information is represented by a color heightmap of the target object. The agent needs to implicitly find the correspon
Most existing imitation learning approaches assume the demonstrations are drawn from experts who are optimal, but relaxing this assumption enables us to use a wider range of data. Standard imitation learning may learn a suboptimal policy from demonstrations with varying optimality. Prior works use c…
In this paper, the configuration transformation of Wheel-Legged Robot (WLR) is studied, which can enable the robot to change its multilinks configuration on Inverted Equilibrium Manifold (IEM), while keeping balance with a small location drift on the floor. First of all, the general form of dynamics…
The performance of traditional hydraulic robots is often limited by their hoses across moving joints or connecting hydraulic drive units, which would reduce their mobility and impede their ability to operate in complex environment. In response to this deficiency, this paper introduces the WLR-II (th…
Wheel-legged hybrid robot with multi-modal locomotion can efficiently adapt to different terrain environments, as well as realize rapid maneuver on flat ground. We have developed a novel hydraulic wheel-legged robot (WLR) combined with a humanoid structural design. This robot can assist to emergency…
Prediction of interaction force between human and contacting environment has always been an important issue for such as robot control and clinical treatment. Although force/torque sensors can provide direct and precise measurement results, in many cases it is inconvenient to attach these sensors on…