Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (2304.13705v1)

Abstract: Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback. Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up. Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks? We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface. Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary. To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences. ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80-90% success, with only 10 minutes worth of demonstrations. Project website: https://tonyzhaozh.github.io/aloha/

• The paper introduces a robust framework that integrates teleoperation and imitation learning for fine-grained bimanual manipulation on low-cost hardware.
• The ALOHA system employs joint-space mapping between leader and follower robots, effectively bypassing complex inverse kinematics while keeping costs under $20,000.
• The novel Action Chunking with Transformers (ACT) algorithm minimizes compounding errors and outperforms existing methods with minimal training data.

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

The paper "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware" presents a systematic approach to enabling precision manipulation using economically accessible robotic hardware. The authors propose a comprehensive framework that incorporates an innovative teleoperation system and a novel imitation learning algorithm. The aim is to achieve precise bimanual manipulation, a challenging task typically reserved for high-end robots due to the precision and coordination required.

Teleoperation System

Central to the approach is the ALOHA (A Low-cost Open-source Hardware System for Bimanual Teleoperation) setup, which leverages two sets of commercially available robotic arms augmented by 3D-printed components. The system is designed to be reproducible, costing under $20,000—a price point that makes it accessible to many research labs.

ALOHA utilizes joint-space mapping between "leader" and "follower" robots to facilitate teleoperation. This method bypasses the complexities of inverse kinematics often required for task-space mapping. The setup includes off-the-shelf sensory equipment optimized for ease of assembly and repairability. Functionality is validated through a suite of tasks demanding high precision and diverse types of interaction, such as threading zip ties and juggling.

Imitation Learning Algorithm

The imitation learning component introduces Action Chunking with Transformers (ACT), a novel algorithm designed to manage the inherent challenges in training policies from human demonstrations, particularly minimizing compounding errors—a common issue in high-precision domains. This is achieved by chunking action sequences to reduce the effective horizon and employing a Transformer-based sequence model enhanced with CVAE (Conditional Variational Autoencoder) training.

Four evaluation setups were presented: two simulated and two real-world tasks. The results demonstrated that ACT, with action chunking and temporal ensembling, significantly outperformed existing algorithms, achieving high success rates with minimal training data.

Implications and Future Directions

This work presents a practical pathway towards democratizing robotic fine manipulation tasks, traditionally constrained by hardware costs. The proposed framework could catalyze developments in various applications, from manufacturing to assistive robotics.

Future research could explore scaling the approach to handle more complex or dynamic tasks. Moreover, integrating advanced perception capabilities to further enhance task robustness, and expanding the scope to include multi-robot systems or collaborative human-robot teams, offer exciting avenues of potential growth.

In conclusion, by combining hardware accessibility with an innovative learning approach, this research contributes a significant advancement in bimanual robot teleoperation and learning. It provides a resourceful template for deploying cost-effective robotic solutions in real-world fine manipulation tasks.