Model-Based Inverse Reinforcement Learning from Visual Demonstrations

2020-10-18 · Neha Das, Sarah Bechtle, Todor Davchev, Dinesh Jayaraman, Akshara Rai, Franziska Meier

Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem. The key challenges lie in learning good dynamics models, developing algorithms that scale to high-dimensional state-spaces and being able to learn from both visual and proprioceptive demonstrations. In this work, we present a gradient-based inverse reinforcement learning framework that utilizes a pre-trained visual dynamics model to learn cost functions when given only visual human demonstrations. The learned cost functions are then used to reproduce the demonstrated behavior via visual model predictive control. We evaluate our framework on hardware on two basic object manipulation tasks.

📄 PDF Abstract BibTeX arXiv:2010.09034

Code (0)

등록된 구현이 없습니다.

Tasks

modelModel Predictive Controlreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Nonlinear Inverse Reinforcement Learning with Gaussian Processes

2011-12-01 · NeurIPS 2011 12 · Sergey Levine, Zoran Popovic, Vladlen Koltun

We present a probabilistic algorithm for nonlinear inverse reinforcement learning. The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. While…

Gaussian Processes reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

SPLASH! Sample-efficient Preference-based inverse reinforcement learning for Long-horizon Adversarial tasks from Suboptimal Hierarchical demonstrations

2025-07-11 · Peter Crowley, Zachary Serlin, Tyler Paine, Makai Mann 외 arxiv

Inverse Reinforcement Learning (IRL) presents a powerful paradigm for learning complex robotic tasks from human demonstrations. However, most approaches make the assumption that expert demonstrations are available, which…

Reinforcement Learning

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables

2019-09-20 · NeurIPS 2019 12 · Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon

Providing a suitable reward function to reinforcement learning can be difficult in many real world applications. While inverse reinforcement learning (IRL) holds promise for automatically learning reward functions from d…

continuous-control Continuous Control reinforcement-learning Reinforcement Learning+1

Option Compatible Reward Inverse Reinforcement Learning

2019-11-07 · Rakhoon Hwang, Hanjin Lee, Hyung Ju Hwang

Reinforcement learning in complex environments is a challenging problem. In particular, the success of reinforcement learning algorithms depends on a well-designed reward function. Inverse reinforcement learning (IRL) so…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Transfer Learning

Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations

2018-01-07 · ICML 2018 7 · Xingyu Wang, Diego Klabjan

This paper considers the problem of inverse reinforcement learning in zero-sum stochastic games when expert demonstrations are known to be not optimal. Compared to previous works that decouple agents in the game by assum…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)