Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation (2412.15109v1)

Abstract: Current efforts to learn scalable policies in robotic manipulation primarily fall into two categories: one focuses on "action," which involves behavior cloning from extensive collections of robotic data, while the other emphasizes "vision," enhancing model generalization by pre-training representations or generative models, also referred to as world models, using large-scale visual datasets. This paper presents an end-to-end paradigm that predicts actions using inverse dynamics models conditioned on the robot's forecasted visual states, named Predictive Inverse Dynamics Models (PIDM). By closing the loop between vision and action, the end-to-end PIDM can be a better scalable action learner. In practice, we use Transformers to process both visual states and actions, naming the model Seer. It is initially pre-trained on large-scale robotic datasets, such as DROID, and can be adapted to realworld scenarios with a little fine-tuning data. Thanks to large-scale, end-to-end training and the synergy between vision and action, Seer significantly outperforms previous methods across both simulation and real-world experiments. It achieves improvements of 13% on the LIBERO-LONG benchmark, 21% on CALVIN ABC-D, and 43% in real-world tasks. Notably, Seer sets a new state-of-the-art on CALVIN ABC-D benchmark, achieving an average length of 4.28, and exhibits superior generalization for novel objects, lighting conditions, and environments under high-intensity disturbances on real-world scenarios. Code and models are publicly available at https://github.com/OpenRobotLab/Seer/.

• The paper proposes an end-to-end PIDM framework that predicts actions using forecasted visual states to integrate vision with control.
• The Seer model employs a Transformer-based architecture with foresight and action tokens, outperforming baselines by 9% on benchmarks.
• Experimental evaluations demonstrated a 43% success rate improvement in real-world tasks, highlighting its scalability and robustness.

Review of "Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation"

The paper "Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation" introduces an innovative paradigm to enhance policy learning in robotic manipulation. The work is centered on Predictive Inverse Dynamics Models (PIDM), a new approach designed to integrate vision and action in robotic control systems.

Summary

This research addresses the limitations of current methodologies by proposing an end-to-end framework that unifies the advantages of behaviour cloning (from extensive robotic data) and model generalization through pre-trained representations or world models. The core contribution lies in the development of an end-to-end PIDM paradigm that predicts actions using inverse dynamics models, conditioned on forecasted visual states, effectively closing the loop between vision and action. The resulting model, Seer, leverages the Transformer architecture to process visual states and actions, which contributes to its scalability.

Methodology

The methodological novel aspect of this research lies in the synergy between the visual prediction and inverse dynamics modules, optimized simultaneously during training. Seer's architecture incorporates two main tokens: a foresight token predicting future RGB images, and an action token estimating intermediate actions between the current and predicted visual observations. Notably, a unidirectional attention mask in Transformer processing enables comprehensive integration of past and foreseen predictive information in an end-to-end fashion.

The dual-stage training process includes pre-training on large-scale datasets such as DROID, followed by fine-tuning on task-specific data with minimal adjustments. This strategy highlights the model's adaptability to real-world scenarios, with significant performance advantages across both simulated and practical applications.

Experimental Evaluation

The model was evaluated on simulation benchmarks such as LIBERO-LONG and CALVIN ABC-D, where it demonstrated robust improvements over existing state-of-the-art solutions. On LIBERO-LONG, Seer outperformed baselines by 9% after pre-training, achieving a success rate of 78.7%. On CALVIN ABC-D, it set new records by solving an average task sequence length of 4.28, thereby instituting a new state-of-the-art benchmark.

Real-world experiments further corroborated these findings, with Seer achieving remarkable improvements in success rates under diverse and high-intensity disturbance conditions. For instance, the model demonstrated a 43% improvement in success rates across challenging tasks, thereby asserting its robustness and efficacy in practical settings.

Implications and Future Directions

The successful deployment of Seer in complex real-world tasks underscores the potential for PIDM to advance generalizable and scalable robotic manipulation. The integration of predictive vision and dynamics presents clear advantages in adaptability and efficiency, suggesting profound implications for the future design of robotic systems. Practical deployments may benefit from reduced training data requirements and enhanced robustness under novel scenarios.

Given these promising results, future research directions could involve exploring cross-embodiment capabilities, enabling applicability across different robotic systems. Moreover, further investigation into task-specific high-precision interactions could expand the scope of PIDM utilizations, potentially transcending current application boundaries.

In conclusion, this work represents a substantial step forward in scalable robotic manipulation, with Seer's architecture providing a compelling template for integrating vision and action through predictive dynamics. Its implications for enhanced efficiency and generalization in robotic systems are significant, warranting further exploration and refinement.