Lessons from Learning to Spin "Pens" (2407.18902v2)

Abstract: In-hand manipulation of pen-like objects is an important skill in our daily lives, as many tools such as hammers and screwdrivers are similarly shaped. However, current learning-based methods struggle with this task due to a lack of high-quality demonstrations and the significant gap between simulation and the real world. In this work, we push the boundaries of learning-based in-hand manipulation systems by demonstrating the capability to spin pen-like objects. We first use reinforcement learning to train an oracle policy with privileged information and generate a high-fidelity trajectory dataset in simulation. This serves two purposes: 1) pre-training a sensorimotor policy in simulation; 2) conducting open-loop trajectory replay in the real world. We then fine-tune the sensorimotor policy using these real-world trajectories to adapt it to the real world dynamics. With less than 50 trajectories, our policy learns to rotate more than ten pen-like objects with different physical properties for multiple revolutions. We present a comprehensive analysis of our design choices and share the lessons learned during development.

• The paper demonstrates a novel reinforcement learning framework integrating simulation, high-fidelity trajectory generation, and real-world fine-tuning.
• It emphasizes the importance of diverse initial state designs and privileged sensory inputs for effective exploration and stability.
• The method successfully bridges the sim-to-real gap, achieving high rotation stability and robust adaptation to varied pen-like objects.

Insights from "Lessons from Learning to Spin 'Pens'"

The paper "Lessons from Learning to Spin 'Pens'" explores the challenge of in-hand manipulation of pen-like objects using reinforcement learning (RL) and sim-to-real techniques. The task is particularly demanding due to the dynamic nature of keeping a pen spinning smoothly, which requires sophisticated finger coordination and the ability to adapt to varying physical properties of different objects. This paper contributes a novel approach that circumvents the limitations faced by existing learning-based methods.

Key Contributions

The authors present a structured approach that integrates simulation training, high-fidelity trajectory generation, and fine-tuning using real-world data. The main contributions are outlined as follows:

1. Oracle Policy Training in Simulation:
   • The authors use reinforcement learning to train an oracle policy in a simulation environment equipped with extensive sensory inputs, including joint positions, tactile signals, and point clouds.
   • They emphasize the importance of a well-designed initial state distribution to facilitate exploration and stabilization during training.
   • The reward structure includes not only rotation velocity but also a z-reward to maintain the pen's horizontal stability, which is essential for successful real-world transfer.
2. Pre-training Sensorimotor Policy:
   • The transition from simulation to realities such as decoding proprioception or visuotactile feedback often presents challenges due to the sim-to-real gap.
   • Instead of following traditional distillation approaches, the sensorimotor policy is pre-trained using a dataset of proprioceptive inputs and corresponding actions, collected through the well-functioning oracle policy in simulation.
   • This method exposes the policy to a broad range of contexts, providing a robust motion prior necessary for subsequent real-world fine-tuning.
3. Fine-Tuning with Real-World Data:
   • To bridge the sim-to-real gap, the authors collect real-world trajectories using the oracle policy as an open-loop controller to generate high-fidelity demonstrations.
   • These demonstrations are limited to fewer than 50 trajectories but are essential for adapting the pre-trained sensorimotor policy to real-world dynamics effectively.

Experimental Results

The experimental evaluation includes comprehensive tests both in simulation and the real world, revealing several critical insights:

1. Initial State Design:
   • Multiple canonical initial poses were used to enhance exploration during training. Policies trained with these poses vastly outperformed those that used a single pose, demonstrating smoother and stable rotations.
2. Significance of Privileged Information:
   • The inclusion of extensive sensory inputs such as tactile feedback and object shape encoded via a point cloud significantly improves the learning outcome of the oracle policy.
   • Policies trained without privileged information failed to achieve the necessary performance even in simulation, indicating the importance of such data in learning complex tasks.
3. Sim-to-Real Transfer Techniques:
   • Direct deployment of the oracle policy or traditional distillation to sensorimotor policies did not yield satisfactory real-world performance.
   • The proposed approach of using real-world trajectories for fine-tuning was effective, achieving higher rotation stability and generalization across different objects.

Quantitatively, the proposed method showed superior performance with high success rates and consistently better adaptation to unseen objects compared to other baselines. Notably, the sensorimotor policy pre-trained in simulation, when fine-tuned with real-world data, displayed marked improvements in handling diverse and out-of-distribution objects.

Implications and Future Work

The implications of this research are multifaceted. Practically, the ability to spin pens robustly using learning-based methods can extend to more complex manipulation tasks involving similarly shaped tools. Theoretically, these findings highlight the importance of structured initial state design and the benefit of integrating high-fidelity simulation data with real-world fine-tuning for overcoming the sim-to-real gap.

Future developments could focus on leveraging more advanced sensory feedback, such as enhanced visuotactile signals, to improve fine-tuning fidelity. Additionally, extending this approach to multi-axis rotation and other challenging in-hand manipulation tasks offers a promising direction, potentially expanding the capabilities of robotic manipulation in various practical scenarios.