9 accepted papers
Motion planning for autonomous vision-based car racing is a challenging task in robotics. Classical racing systems divide the task into numerous submodules, undermining computational efficiency and leading to error propagation. Previous studies have demonstrated impressive reinforcement learning (RL…
A key challenge in Meta-Reinforcement Learning (meta-RL) is the task distribution shift, since the generalization ability of most current meta-RL methods is limited to tasks sampled from the training distribution. In this paper, we propose Posterior Sampling Bayesian Lifelong In-Context Reinforcemen…
Aerial-aquatic vehicles are capable to move in the two most dominant fluids, making them more promising for a wide range of applications. We propose a prototype with special designs for propulsion and thruster configuration to cope with the vast differences in the fluid properties of water and air.…
Manipulation in a densely cluttered environment creates complex challenges in perception to close the control loop, many of which are due to the sophisticated physical interaction between the environment and the manipulator. Drawing from biological sensory-motor control, to handle the task in such a…
Nowadays, robots are increasingly used in various surgery applications. Meanwhile, in many of these applications, the operation tools of surgical robots need to be in contact with human soft tissues. Furthermore, this tool-soft tissue interaction brings great challenges to robot control and system p
Robotic systems are increasingly required not only to generate precise motions to complete their tasks but also to handle the interactions with the environment or human. Significantly, soft interaction brings great challenges on the force control due to the nonlinear, viscoelastic and inhomogeneous…
Due to the nonholonomic constraints as well as the constraints on torque and power resources, it is challenging to design a controller to asymptotically stabilize a nonholonomic robot at a predefined pose. In this paper, a switched controller is introduced for the asymptotic stabilization of nonholo
In this work, we propose the Depth Generation Network (DGN) to address the problem of dense depth estimation by exploiting the variational method and the deep-learning technique. In particular, we focus on improving the feasibility of depth estimation under complex scenarios given stereo RGB images,…
Soft robots have recently evoked extensive attention due to their abilities to work effectively in unstructured environments. As an actuation technology of soft robots, dielectric elastomers exhibit many intriguing attributes such as large strain and high energy density. This work presents a novel d…