# DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands

arXiv: https://arxiv.org/abs/2310.08809 · HTML: https://mlsanity.com/paper/2310.08809/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 24 of 24)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [Learning agile and dynamic motor skills for legged robots](https://mlsanity.com/paper/1901.08652/) (2019)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [Learning robust perceptive locomotion for quadrupedal robots in the wild](https://mlsanity.com/paper/2201.08117/) (2022)
- [A System for General In-Hand Object Re-Orientation](https://mlsanity.com/paper/2111.03043/) (2021)
- [TossingBot: Learning to Throw Arbitrary Objects with Residual Physics](https://mlsanity.com/paper/1903.11239/) (2019)
- [Model Predictive Path Integral Control using Covariance Variable Importance Sampling](https://mlsanity.com/paper/1509.01149/) (2015)
- [Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning](https://mlsanity.com/paper/2206.08686/) (2022)
- [A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement Learning](https://mlsanity.com/paper/2208.07860/) (2022)
- [Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning](https://mlsanity.com/paper/2111.03062/) (2021)
- [Stabilizing Neural Control Using Self-Learned Almost Lyapunov Critics](https://mlsanity.com/paper/2107.04989/) (2021)
- [Dynamic Handover: Throw and Catch with Bimanual Hands](https://mlsanity.com/paper/2309.05655/) (2023)
- [DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training](https://mlsanity.com/paper/2305.12127/) (2023)
- [Is High Variance Unavoidable in RL? A Case Study in Continuous Control](https://mlsanity.com/paper/2110.11222/) (2021)
- [Learning Dexterous Manipulation from Suboptimal Experts](https://mlsanity.com/paper/2010.08587/) (2020)
- [Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2206.12030/) (2022)
- [Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning](https://mlsanity.com/paper/2009.05104/) (2020)

## Built on by (most cited first, 8 of 8)

- [Morphologically Symmetric Reinforcement Learning for Ambidextrous Bimanual Manipulation](https://mlsanity.com/paper/2505.05287/) (2025)
- [Bimanual Grasp Synthesis for Dexterous Robot Hands](https://mlsanity.com/paper/2411.15903/) (2024)
- [Learning Dexterous Bimanual Catch Skills through Adversarial-Cooperative Heterogeneous-Agent Reinforcement Learning](https://mlsanity.com/paper/2502.11437/) (2025)
- [VacuumVLA: Boosting VLA Capabilities via a Unified Suction and Gripping Tool for Complex Robotic Manipulation](https://mlsanity.com/paper/2511.21557/) (2025)
- [SpikePingpong: Spike Vision-based Fast-Slow Pingpong Robot System](https://mlsanity.com/paper/2506.06690/) (2025)
- [Nightmare Dreamer: Dreaming About Unsafe States And Planning Ahead](https://mlsanity.com/paper/2601.04686/) (2026)
- [SIAgent: Spatial Interaction Agent via LLM-powered Eye-Hand Motion Intent Understanding in VR](https://mlsanity.com/paper/2603.00522/) (2026)
- [Tele-Catch: Adaptive Teleoperation for Dexterous Dynamic 3D Object Catching](https://mlsanity.com/paper/2603.28427/) (2026)

## Abstract (arXiv)

Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throwing-catching behavior has the potential to increase the speed of transporting objects to their destination. However, dynamic dexterous manipulation poses a major challenge for stable control due to a large number of dynamic contacts. In this paper, we propose a Learning-based framework for Throwing-Catching tasks using dexterous hands (LTC). Our method, LTC, achieves a 73% success rate across 45 scenarios (diverse hand poses and objects), and the learned policies demonstrate strong zero-shot transfer performance on unseen objects. Additionally, in tasks where the object in hand faces sideways, an extremely unstable scenario due to the lack of support from the palm, all baselines fail, while our method still achieves a success rate of over 60%.