# Visual IRL for Human-Like Robotic Manipulation

arXiv: https://arxiv.org/abs/2412.11360 · HTML: https://mlsanity.com/paper/2412.11360/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 16 of 16)

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [AI Feynman: a Physics-Inspired Method for Symbolic Regression](https://mlsanity.com/paper/1905.11481/) (2019)
- [Learning Robust Rewards with Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1710.11248/) (2017)
- [Discovering Symbolic Models from Deep Learning with Inductive Biases](https://mlsanity.com/paper/2006.11287/) (2020)
- [A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress](https://mlsanity.com/paper/1806.06877/) (2018)
- [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017)
- [Neural Symbolic Regression that Scales](https://mlsanity.com/paper/2106.06427/) (2021)
- [One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning](https://mlsanity.com/paper/1802.01557/) (2018)
- [Visual Imitation Made Easy](https://mlsanity.com/paper/2008.04899/) (2020)
- [DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video](https://mlsanity.com/paper/2202.00164/) (2022)
- [Model-Based Inverse Reinforcement Learning from Visual Demonstrations](https://mlsanity.com/paper/2010.09034/) (2020)
- [Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulation](https://mlsanity.com/paper/2402.07127/) (2024)
- [Simitate: A Hybrid Imitation Learning Benchmark](https://mlsanity.com/paper/1905.06002/) (2019)
- [Marginal MAP Estimation for Inverse RL under Occlusion with Observer Noise](https://mlsanity.com/paper/2109.07788/) (2021)
- [MVSA-Net: Multi-View State-Action Recognition for Robust and Deployable Trajectory Generation](https://mlsanity.com/paper/2311.08393/) (2023)
- [Efficient Skill Acquisition for Complex Manipulation Tasks in Obstructed Environments](https://mlsanity.com/paper/2303.03365/) (2023)

## Built on by (most cited first, 1 of 1)

- [Dexterous Manipulation through Imitation Learning: A Survey](https://mlsanity.com/paper/2504.03515/) (2025)

## Abstract (arXiv)

We present a novel method for collaborative robots (cobots) to learn manipulation tasks and perform them in a human-like manner. Our method falls under the learn-from-observation (LfO) paradigm, where robots learn to perform tasks by observing human actions, which facilitates quicker integration into industrial settings compared to programming from scratch. We introduce Visual IRL that uses the RGB-D keypoints in each frame of the observed human task performance directly as state features, which are input to inverse reinforcement learning (IRL). The inversely learned reward function, which maps keypoints to reward values, is transferred from the human to the cobot using a novel neuro-symbolic dynamics model, which maps human kinematics to the cobot arm. This model allows similar end-effector positioning while minimizing joint adjustments, aiming to preserve the natural dynamics of human motion in robotic manipulation. In contrast with previous techniques that focus on end-effector placement only, our method maps multiple joint angles of the human arm to the corresponding cobot joints. Moreover, it uses an inverse kinematics model to then minimally adjust the joint angles, for accurate end-effector positioning. We evaluate the performance of this approach on two different realistic manipulation tasks. The first task is produce processing, which involves picking, inspecting, and placing onions based on whether they are blemished. The second task is liquid pouring, where the robot picks up bottles, pours the contents into designated containers, and disposes of the empty bottles. Our results demonstrate advances in human-like robotic manipulation, leading to more human-robot compatibility in manufacturing applications.