Abstract
Continual Robot Policy Learning via Variational Neural Dynamics
- Authors
- Jiaxu Xing, Zhiyuan Zhu, Yunfan Ren...
- Categories
- cs.RO
Brief
A continual learning framework for robot control learns condition-aware dynamics by combining an analytical physics prior with a neural residual and a recurrent encoder that infers hidden, recurring dynamics from recent experience. Policies are trained in differentiable simulation over sampled latent conditions and at deployment use online inference to recover recurring disturbances. In quadrotor tests under changing wind, the approach achieves ~1 s recovery and major error reductions compared to SOTA.
Why it matters
Proposes a continual learning framework that combines an analytical physics prior with a neural residual and a recurrent encoder that infers hidden, recurring dynamics from recent state-action trajectories; the inferred latent conditions condition both the residual dynamics model and the policy, and policy learning is done via differentiable simulation over sampled latent dynamics.
Key details
- Real quadrotor experiments under changing wind: the policy recovers from recurring disturbances in ≈1 s (≈5× faster than online residual re-fitting) and reduces large-disturbance hover and tracking errors by 65.7% and 53.3%, respectively, versus state-of-the-art online adaptation.