Multi-goal RL benchmark paper includes in-hand manipulation with Shadow Dexterous Hand
Why it matters
External product-linked research evidence for a robotics product profile.
Evidence notes
- The report introduces OpenAI Gym continuous-control environments based on existing robotics hardware, including Fetch arm tasks and in-hand manipulation with a Shadow Dexterous Hand.
- The Shadow hand environment uses sparse binary rewards and a multi-goal reinforcement-learning setup in which the desired manipulation goal is supplied as an additional input.
- The report also proposes research directions for multi-goal reinforcement learning and Hindsight Experience Replay.
Company context
Shadow Robot develops dexterous robotic hands, tactile sensing and teleoperation systems for manipulation research, embodied-AI labs and advanced robot-control work.