間接観測からの状態推定と動力学学習を同時に行う手法STEADY
STEADY: Simultaneous State Estimation and Dynamics Learning from Indirect Observations
ノイズの多い間接観測からパーティクルフィルタとEMループを用いて状態推定とニューラル動力学モデル学習を同時に進める手法を提案し、シミュレーションと実機で高精度かつ観測ノイズに頑健であることを示した。
著者: Jiayi Wei, Jarrett Holtz, Isil Dillig, Joydeep Biswas
分類: cs.RO, cs.LG
原文アブストラクト
Accurate kinodynamic models play a crucial role in many robotics applications such as off-road navigation and high-speed driving. Many state-of-the-art approaches in learning stochastic kinodynamic models, however, require precise measurements of robot states as labeled input/output examples, which can be hard to obtain in outdoor settings due to limited sensor capabilities and the absence of ground truth. In this work, we propose a new technique for learning neural stochastic kinodynamic models from noisy and indirect observations by performing simultaneous state estimation and dynamics learning. The proposed technique iteratively improves the kinodynamic model in an expectation-maximization loop, where the E Step samples posterior state trajectories using particle filtering, and the M Step updates the dynamics to be more consistent with the sampled trajectories via stochastic gradient ascent. We evaluate our approach on both simulation and real-world benchmarks and compare it with several baseline techniques. Our approach not only achieves significantly higher accuracy but is also more robust to observation noise, thereby showing promise for boosting the performance of many other robotics applications.
