Task-oriented grasping for dexterous robots using postural synergies and reinforcement learning
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
In this paper, we address the problem of task-oriented grasping for humanoid robots, emphasizing the need to align with human social norms and task-specific objectives. Existing methods, employ a variety of open-loop and closed-loop approaches but lack an end-to-end solution that can grasp several objects while taking into account the downstream task's constraints. Our proposed approach employs reinforcement learning to enhance task-oriented grasping, prioritizing the post-grasp intention of the agent. We extract human grasp preferences from the ContactPose dataset, and train a hand synergy model based on the Variational Autoencoder (VAE) to imitate the participant's grasping actions. Based on this data, we train an agent able to grasp multiple objects while taking into account distinct post-grasp intentions that are task-specific. By combining data-driven insights from human grasping behavior with learning by exploration provided by reinforcement learning, we can develop humanoid robots capable of context-aware manipulation actions, facilitating collaboration in human-centered environments.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 14 references that are in the map. First posted on arXiv 24 Feb 2026.
Builds on
7 of 14 references.
References · 7
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2020ContactPose: A Dataset of Grasps with Object Contact and Hand Posecited by 183
- 2019ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imagingcited by 147
- 2022DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Videocited by 89
- 2020Learning Dexterous Grasping with Object-Centric Visual Affordancescited by 85
- 2020Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Graspingcited by 27
- 2019CAGE: Context-Aware Grasping Enginecited by 10
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- 2025Composing Dextrous Grasping and In-hand Manipulation via Scoring with a Reinforcement Learning Critic
- 2025Learning Adaptive Dexterous Grasping from Single Demonstrations
- 2025Coordinated Humanoid Manipulation with Choice Policies
- 2024Cross-Embodiment Dexterous Grasping with Reinforcement Learning
- 2025Interactive Imitation Learning for Dexterous Robotic Manipulation: Challenges and Perspectives -- A Survey
- 2025Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids
- 2025EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignment
- 2025COMBO-Grasp: Learning Constraint-Based Manipulation for Bimanual Occluded Grasping