Active Visuo-Haptic Object Shape Completion (2203.09149v1)

Abstract: Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high uncertainty in the occluded object parts, which negatively impacts the performance of downstream robotic tasks such as grasping. In this work, we propose an active visuo-haptic shape completion method called Act-VH that actively computes where to touch the objects based on the reconstruction uncertainty. Act-VH reconstructs objects from point clouds and calculates the reconstruction uncertainty using IGR, a recent state-of-the-art implicit surface deep neural network. We experimentally evaluate the reconstruction accuracy of Act-VH against five baselines in simulation and in the real world. We also propose a new simulation environment for this purpose. The results show that Act-VH outperforms all baselines and that an uncertainty-driven haptic exploration policy leads to higher reconstruction accuracy than a random policy and a policy driven by Gaussian Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the best reconstruction baseline on grasping 10 novel objects. The results show that Act-VH reaches a significantly higher grasp success rate than the baseline on all objects. Together, this work opens up the door for using active visuo-haptic shape completion in more complex cluttered scenes.

• The paper introduces a novel technique that integrates visual data with haptic feedback to overcome occlusion challenges in shape reconstruction.
• It employs an Implicit Geometric Regularization model to generate and evaluate shape hypotheses using uncertainty estimates for guided exploration.
• Experimental results show an 80% grasp success rate, significantly outperforming traditional methods and confirming its practical robotic application.

Active Visuo-Haptic Object Shape Completion: An Expert Review

The paper "Active Visuo-Haptic Object Shape Completion" explores a novel methodology that integrates both visual and haptic data for object shape reconstruction. This approach addresses the intrinsic limitations of using purely visual data, which suffers from occlusions and results in uncertainty in the occluded parts affecting downstream tasks like robotic grasping.

The proposed method, Act-VH, hinges on incorporating haptic feedback informed by evaluating reconstruction uncertainty. This feedback guides the robotic exploration strategy, specifically determining where the robot should touch the object next. The foundational model employed is the Implicit Geometric Regularization (IGR), an advanced deep implicit surface network, which enables comprehensive shape reconstruction from point clouds. This network efficiently generates multiple hypothetical shape samples, which are evaluated to determine the uncertainty zones. The exploration and data collection strategy is then adjusted in response to these uncertainty estimates, allowing for improvements in resultant shape reconstructions compared to purely heuristic or random exploration methods.

Numerical Results and Evaluations

The empirical evaluation of Act-VH was conducted against five established baseline methods: Ball Pivoting Algorithm, Poisson Reconstruction, Convex Hull Reconstruction, Alpha Shapes, and Gaussian Process Implicit Surfaces. The experimental results convincingly demonstrate that Act-VH achieves superior reconstruction accuracy, both in simulation and real-world settings. Specifically, the average grasp success rate after five haptic explorations reached 80%, significantly outperforming the next best baseline, which achieved only 46.7%. These findings underscore the effectiveness of integrating active haptic exploration driven by uncertainty assessments.

Furthermore, Act-VH's robustness was reflected in its adaptability across different object shapes in both simulated and real environments. The Chamfer distance and Jaccard similarity metrics used to evaluate reconstruction fidelity consistently favored Act-VH over baseline methods. These numerical performances highlight Act-VH's potential practical application in robotics, particularly in scenarios demanding high precision in object handling.

Implications and Future Directions

The theoretical advancement presented in this paper extends beyond reconstruction accuracy, potentially influencing object manipulation in cluttered environments where occlusion is prevalent. The active visuo-haptic approach is particularly relevant for robotics applications in unstructured environments, including autonomous personal assistance robotics and industrial automation tasks where manipulation of occluded objects is routine.

Further explorations could focus on enhancing shape reconstruction accuracy by incorporating negative inference points (where surfaces do not exist) into the loss function of the IGR network. Additionally, future work might address the optimization of robot exploration strategies to account for uncertainties in haptic feedback, further enhancing robustness and accuracy.

In summary, this paper makes a significant contribution to the field of shape completion by effectively leveraging both visuo-haptic modalities, setting the stage for more efficient and accurate robotic interaction with complex environments. The insights offered could inspire further advancement in integrated sensory modalities, combining vision and touch for a comprehensive understanding of and interaction with the physical world.