Leveraging Embodied Mechanical Intelligence for Learning Decluttering Tasks
Enrico Turco, Valerio Bo, Chiara Castellani, Gionata Salvietti, Monica Malvezzi, Domenico Prattichizzo, Maria Pozzi
AI summary
Problem
Most learning-based decluttering methods rely on rigid grippers and complex multi-step policies, overlooking how specialized gripper morphology can simplify robotic learning.
Approach
The authors train a deep reinforcement learning policy to declutter scenes using three grippers, comparing a simplified grasp-only strategy against a traditional push-grasp approach in simulation and real-world tests.
Key results
- Soft ScoopGripper achieved higher sample efficiency with a grasp-only policy
- Scoop morphology enabled non-prehensile motions during grasping
- Grasp-only policy converged faster than push-grasp across all tests
- Real-world trials confirmed simulation-based learning advantages
Why it matters
Proves that task-specific mechanical design can drastically reduce the complexity and data needs of robotic learning, guiding efficient gripper development for unstructured environments.
Abstract
In this work, we investigate how a state-of-the-art grasp planner based on deep reinforcement learning performs when applied to a soft-rigid gripper in a decluttering task. The gripper, called Soft ScoopGripper, is endowed with a rigid scoop- shaped part that facilitates the interaction with the environment and with objects. We hypothesize that the clever design of such a gripper can facilitate the learning process, reducing the number of required training steps and eliminating the need for learning non-prehensile actions, such as pushing. To validate our hypothesis, we conducted experiments in both simulated and real- world environments, comparing the selected gripper with a rigid parallel-jaw gripper and a four-fingered soft gripper. Results show that the Soft ScoopGripper learns to effectively declutter scenes using a single action (grasping) instead of two (pushing and grasping). This is due to the fact that the scoop-shaped add- on allows to perform non-prehensile motions during the grasp action.