Toward Global Intent Inference for Human Motion by Inverse Reinforcement Learning
On the map: Offline RL and imitation learning from data → Inverse reinforcement learning
Abstract (arXiv)
This paper investigates whether a single, unified cost function can explain and predict human reaching movements, in contrast with existing approaches that rely on subject- or posture-specific optimization criteria. Using the Minimal Observation Inverse Reinforcement Learning (MO-IRL) algorithm, together with a seven-dimensional set of candidate cost terms, we efficiently estimate time-varying cost weights for a standard planar reaching task. MO-IRL provides orders-of-magnitude faster convergence than bilevel formulations, while using only a fraction of the available data, enabling the practical exploration of time-varying cost structures. Three levels of generality are evaluated: Subject-Dependent Posture-Dependent, Subject-Dependent Posture-Independent, and Subject-Independent Posture-Independent. Across all cases, time-varying weights substantially improve trajectory reconstruction, yielding an average 27% reduction in RMSE compared to the baseline. The inferred costs consistently highlight a dominant role for joint-acceleration regulation, complemented by smaller contributions from torque-change smoothness. Overall, a single subject- and posture-agnostic time-varying cost function is shown to predict human reaching trajectories with high accuracy, supporting the existence of a unified optimality principle governing this class of movements.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 5 of its 39 references that are in the map. First posted on arXiv 8 Mar 2026.
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5 of 39 references.
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- 2025Cost Function Estimation Using Inverse Reinforcement Learning with Minimal Observations13 May
- 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress18 Jun
Other references · 3
- 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
- 2019Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Controlcited by 29
- 2025Assessing the Quality of a Set of Basis Functions for Inverse Optimal Control via Projection onto Global Minimizerscited by 1
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- 2025Learning Human Reaching Optimality Principles from Minimal Observation Inverse Reinforcement Learning
- 2020Learning Navigation Costs from Demonstration in Partially Observable Environments
- 2020Model-Based Inverse Reinforcement Learning from Visual Demonstrations
- 2021Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning
- 2024Robotic Arm Manipulation with Inverse Reinforcement Learning & TD-MPC
- 2020Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization
- 2021A Generalised Inverse Reinforcement Learning Framework
- 2019RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration