Natural Sit-to-Stand Motion Synthesis For Humanoids via Guided Assistance Curricula and Staged Rewards
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
A humanoid has infinitely many ways to stand up from sitting while maintaining balance, making sit-to-stand (STS) a challenging control problem. We synthesise natural humanoid STS motion from scratch using reinforcement learning, without demonstrations or reference trajectories. A single Proximal Policy Optimisation policy learns smooth, human-like rising driven by three complementary components. (i) A coupled force/chair-height curriculum is used. A vertical pelvis-assist force aids early trajectory exploration and decays over training. Taller chairs are unlocked with decaying assisting force. This ensures that the policy masters a viable STS trajectory at each chair height before being exposed to harder ones, avoiding the premature distribution shift that otherwise collapses generalisation. (ii) Motion robustness is achieved by randomly sampling from a large number of inverse kinematics-generated initial and target poses spanning over eight chair heights. (iii) A set of rewards is defined inspired from biomechanics and optimal control studies. They shape the robot's angular momentum for seat-off, and enable support-region transition via centre of pressure attraction function to ensure smooth low-effort actuation. On a deterministic force-free evaluator, the policy attains more than 97% balanced-standing success across eight chair heights. The policy generalises smooth motion across chair heights and enables the robot to rise from substantially deep-seated postures as compared to the state of the art.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 17 references that are in the map. First posted on arXiv 21 Aug 2026.
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- 2026Embedding Classical Balance Control Principles in Reinforcement Learning for Humanoid Recovery9 Mar
Other references · 5
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2025BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusioncited by 165
- 2025Visual Imitation Enables Contextual Humanoid Controlcited by 68
- 2022Predictive Sampling: Real-time Behaviour Synthesis with MuJoCocited by 47
- 2025Learning Humanoid Standing-up Control across Diverse Posturescited by 46
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