5 accepted papers
Humanoid robots are expected to operate reliably over long horizons while executing versatile whole-body skills. Yet Reinforcement Learning (RL) motion policies typically lose stability under prolonged operation, sensor/actuator noise, and real world disturbances. In this work, we propose a Selectiv
Legged robots offer significant potential for navigating complex industrial terrains, but their capabilities are often constrained by perception systems struggling to interpret intricate 3D geometry. Conventional 2D/2.5D representations like depth or elevation maps fail to capture complex 3D geometr…
In this paper, we present a navigation framework for wheeled robots operating in uneven environments. Considering the sparsity of the moving surface along the Z-axis, we introduce a novel map representation, namely Surfelgrid. Surfelgrid uses surfels to represent geometric features of the local area
In robotic reinforcement learning, the Sim2Real gap remains a critical challenge. However, the impact of Static friction on Sim2Real has been underexplored. Conventional domain randomization methods typically exclude Static friction from their parameter space. In our robotic reinforcement learning t
Currently, most table tennis robots concentrate on the canonical position control problem while ignoring the actual velocity control requirements. In this paper, we consider these requirements and propose a new table tennis robot framework. First, a tailor-made mechanical structure is designed such…