mlsanityData to 7 Oct 2026

Robotic Arm Manipulation with Inverse Reinforcement Learning & TD-MPC

arXiv 2407.12941 ↗First posted 17 Jul 2024cs.ROcited by 0

On the map: Offline RL and imitation learning from data → Real-world robot offline RL

Abstract (arXiv)

One unresolved issue is how to scale model-based inverse reinforcement learning (IRL) to actual robotic manipulation tasks with unpredictable dynamics. The ability to learn from both visual and proprioceptive examples, creating algorithms that scale to high-dimensional state-spaces, and mastering strong dynamics models are the main obstacles. In this work, we provide a gradient-based inverse reinforcement learning framework that learns cost functions purely from visual human demonstrations. The shown behavior and the trajectory is then optimized using TD visual model predictive control(MPC) and the learned cost functions. We test our system using fundamental object manipulation tasks on hardware.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Real-world robot offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 6 of its 12 references that are in the map. First posted on arXiv 17 Jul 2024.

Builds on

6 of 12 references.

References · 6

  1. 2018An Algorithmic Perspective on Imitation Learningcited by 455
  2. 2016Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimizationcited by 400
  3. 2016Deep Visual Foresight for Planning Robot Motioncited by 276
  4. 2018Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Controlcited by 220
  5. 2019Unsupervised Learning of Object Keypoints for Perception and Controlcited by 109
  6. 2019Generalized Inner Loop Meta-Learningcited by 92

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  1. 2020Model-Based Inverse Reinforcement Learning from Visual Demonstrations
  2. 2024Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
  3. 2026Online World Modeling Enables Real-World Inverse Reinforcement Learning from Observation
  4. 2024Visual IRL for Human-Like Robotic Manipulation
  5. 2023Plan-Guided Reinforcement Learning for Whole-Body Manipulation
  6. 2024Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation
  7. 2026Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards
  8. 2025Learning Human Reaching Optimality Principles from Minimal Observation Inverse Reinforcement Learning