3 accepted papers
Whole-body loco-manipulation for quadruped robots with arms remains a challenging problem, particularly in achieving multi-task control. To address this, we propose MLM, a reinforcement learning framework driven by both real-world and simulation data. It enables a six-DoF robotic arm–equipped quadru
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 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