MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From Egocentric Videos (2504.06084v1)

Abstract: Large-scale egocentric video datasets capture diverse human activities across a wide range of scenarios, offering rich and detailed insights into how humans interact with objects, especially those that require fine-grained dexterous control. Such complex, dexterous skills with precise controls are crucial for many robotic manipulation tasks, yet are often insufficiently addressed by traditional data-driven approaches to robotic manipulation. To address this gap, we leverage manipulation priors learned from large-scale egocentric video datasets to improve policy learning for dexterous robotic manipulation tasks. We present MAPLE, a novel method for dexterous robotic manipulation that exploits rich manipulation priors to enable efficient policy learning and better performance on diverse, complex manipulation tasks. Specifically, we predict hand-object contact points and detailed hand poses at the moment of hand-object contact and use the learned features to train policies for downstream manipulation tasks. Experimental results demonstrate the effectiveness of MAPLE across existing simulation benchmarks, as well as a newly designed set of challenging simulation tasks, which require fine-grained object control and complex dexterous skills. The benefits of MAPLE are further highlighted in real-world experiments using a dexterous robotic hand, whereas simultaneous evaluation across both simulation and real-world experiments has remained underexplored in prior work.

Analyzing MAPLE: Leveraging Egocentric Videos for Dexterous Robotic Manipulation

The manuscript titled "MAPLE: Encoding Dexterous Robotic Manipulation Priors Learned From Egocentric Videos" presents a novel approach to enhance dexterous robotic manipulation through the use of large-scale egocentric video data. The authors address a critical gap in traditional data-driven robotic manipulation methodologies by incorporating manipulation priors derived from human-activity-rich video datasets to improve robotic manipulation tasks. The proposed solution, MAPLE (Manipulation Priors Learned from Egocentric Videos), is designed to efficiently encode manipulation priors by analyzing human-object interactions in egocentric video settings, facilitating policy learning and improving task generalization.

Key Contributions

1. Innovative Learning Framework: MAPLE employs a dual-stage learning process to predict hand-object contact points and detailed hand poses from single-frame inputs. This process involves a visual encoder-decoder architecture that extracts manipulation-relevant features, subsequently utilized in training downstream robotic manipulation policies.
2. Enhanced Simulation Environments: The authors have developed a set of novel simulated manipulation tasks that offer much-needed diversity and complexity to existing benchmarks, highlighting the broader applicability and effectiveness of MAPLE in various challenging scenarios.
3. Generalization and Real-World Evaluation: The comprehensive evaluation process includes extensive simulation tasks and real-world robotic experiments, demonstrating the enhanced performance and generalization capabilities of policies developed using MAPLE-encoded features.

Methodological Approach

MAPLE stands out by targeting the learning of specific low-level interaction cues, such as contact points and detailed hand poses, which are invaluable for complex manipulation tasks. This method diverges from general-purpose representations that often lack manipulation-specific information. The training process is self-supervised, relying on state-of-the-art tools for automatic supervision extraction, thus obviating the need for labor-intensive human annotations.

The core feature of MAPLE is its predictive ability from egocentric perspectives, capitalizing on extensive datasets like Ego4D to encapsulate human manipulation patterns that can be transposed to robotic settings. This involves leveraging existing reconstruction methodologies to refine and tokenize joint configurations, facilitating the direct application in robotic control contexts.

Experimental Results

In simulations, MAPLE demonstrates strong performance across both traditional and newly introduced dexterous tasks, indicating superior generalization capability compared to existing methods. Furthermore, the translation from simulation to real-world scenarios is seamless, with MAPLE-encoded features significantly enhancing task execution in diverse, realistic settings, as observed in high success rates across several dexterous real-world tasks.

Implications and Future Prospects

The utilization of egocentric videos for training robotic systems represents a promising direction for advancing skill acquisition in robotics. By emulating human manipulation strategies encoded in visual data, systems developed using MAPLE can potentially achieve greater autonomy and effectiveness in unstructured and dynamic environments. Future research avenues could explore integrating language-based conditioning to further refine context-awareness and decision-making processes, as well as extending the range of manipulation tasks covered during training.

MAPLE's methodology suggests a burgeoning potential for cross-disciplinary insights from human-centered video analytics to enhance robotic manipulation, fostering new developments in the field of dexterous robotics. The public release of the code along with the new benchmarking suite will undoubtedly inspire further innovations and refinements, steering elemental advancements in the field.