# Benchmarking Structured Policies and Policy Optimization for Real-World Dexterous Object Manipulation

arXiv: https://arxiv.org/abs/2105.02087 · HTML: https://mlsanity.com/paper/2105.02087/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2021.

## Built on by (most cited first, 15 of 15)

- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Evaluating Real-World Robot Manipulation Policies in Simulation](https://mlsanity.com/paper/2405.05941/) (2024)
- [SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion](https://mlsanity.com/paper/2209.03855/) (2022)
- [Train Offline, Test Online: A Real Robot Learning Benchmark](https://mlsanity.com/paper/2306.00942/) (2023)
- [Transferring Dexterous Manipulation from GPU Simulation to a Remote Real-World TriFinger](https://mlsanity.com/paper/2108.09779/) (2021)
- [Compositional Multi-Object Reinforcement Learning with Linear Relation Networks](https://mlsanity.com/paper/2201.13388/) (2022)
- [Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning](https://mlsanity.com/paper/2105.04962/) (2021)
- [The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents](https://mlsanity.com/paper/2107.05686/) (2021)
- [On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis](https://mlsanity.com/paper/2302.10433/) (2023)
- [Neural Robot Dynamics](https://mlsanity.com/paper/2508.15755/) (2025)
- [Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research](https://mlsanity.com/paper/2204.10297/) (2022)
- [Solving the Real Robot Challenge using Deep Reinforcement Learning](https://mlsanity.com/paper/2109.15233/) (2021)
- [Dexterous Robotic Manipulation using Deep Reinforcement Learning and Knowledge Transfer for Complex Sparse Reward-based Tasks](https://mlsanity.com/paper/2205.09683/) (2022)
- [Reinforcement Learning for Robust Athletic Intelligence: Lessons from the 2nd 'AI Olympics with RealAIGym' Competition](https://mlsanity.com/paper/2503.15290/) (2025)
- [A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics](https://mlsanity.com/paper/2411.05718/) (2024)

## Abstract (arXiv)

Dexterous manipulation is a challenging and important problem in robotics. While data-driven methods are a promising approach, current benchmarks require simulation or extensive engineering support due to the sample inefficiency of popular methods. We present benchmarks for the TriFinger system, an open-source robotic platform for dexterous manipulation and the focus of the 2020 Real Robot Challenge. The benchmarked methods, which were successful in the challenge, can be generally described as structured policies, as they combine elements of classical robotics and modern policy optimization. This inclusion of inductive biases facilitates sample efficiency, interpretability, reliability and high performance. The key aspects of this benchmarking is validation of the baselines across both simulation and the real system, thorough ablation study over the core features of each solution, and a retrospective analysis of the challenge as a manipulation benchmark. The code and demo videos for this work can be found on our website (https://sites.google.com/view/benchmark-rrc).