Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach (2505.06182v2)

Abstract: Humans make extensive use of haptic exploration to map and identify the properties of the objects that we touch. In robotics, active tactile perception has emerged as an important research domain that complements vision for tasks such as object classification, shape reconstruction, and manipulation. This work introduces TAP (Task-agnostic Active Perception) -- a novel framework that leverages reinforcement learning (RL) and transformer-based architectures to address the challenges posed by partially observable environments. TAP integrates Soft Actor-Critic (SAC) and CrossQ algorithms within a unified optimization objective, jointly training a perception module and decision-making policy. By design, TAP is completely task-agnostic and can, in principle, generalize to any active perception problem. We evaluate TAP across diverse tasks, including toy examples and realistic applications involving haptic exploration of 3D models from the Tactile MNIST benchmark. Experiments demonstrate the efficacy of TAP, achieving high accuracies on the Tactile MNIST haptic digit recognition task and a tactile pose estimation task. These findings underscore the potential of TAP as a versatile and generalizable framework for advancing active tactile perception in robotics.

Active Perception for Tactile Sensing: A Task-Agnostic, Attention-Based Approach

The paper "Active Perception for Tactile Sensing: A Task-Agnostic Attention-Based Approach" presents a sophisticated method known as Task-Agnostic Active Perception (TAP), which integrates reinforcement learning (RL) and transformer-based architectures to enhance active tactile perception in robotics. This research situates itself in the evolving domain of utilizing active perception as a robust solution for applications that require tactile sensing in partially observable environments.

Overview and Methodology

Tactile sensing, a crucial modality for object interaction in robotics, often provides localized, detailed contact information but lacks the breadth of vision-based modalities. To fully leverage tactile data, active perception allows for dynamic, task-driven exploration strategies. The proposed TAP framework specifically addresses this need by utilizing a hybrid approach combining RL techniques and attention mechanisms through transformers to jointly train perception modules and decision-making policies. TAP incorporates Soft Actor-Critic (SAC) and CrossQ algorithms, enabling the formulation of a unified optimization objective, promoting generality, and ensuring adaptability across various tactile tasks.

TAP's design intention is task-agnostic, meaning it aims to generalize across tasks without requiring tailor-made adaptations. This feature differentiates it from existing methods that tend to specialize in specific tasks or environmental settings. TAP operates under a Partially Observable Markov Decision Process (POMDP) framework, which facilitates modeling of environments where sensor observations might be partial or noisy.

Experimental Validation

The efficacy of TAP is evaluated through numerous benchmarks, including simulated scenarios like Circle-Square, Tactile MNIST, Starstruck, and Toolbox tasks. These tasks vary in complexity from basic shape recognition to discerning the number of specific objects amidst distractors and estimating the pose of tools. Each task necessitates effective exploration for accurate perception and decision-making, furnishing a comprehensive testbed for assessing TAP's capabilities.

Significant findings arise from these evaluations. For instance, TAP outperforms previous models, such as the Haptic Attention Model, particularly in contexts that demand systematic exploration strategies. TAP's high final prediction accuracies on the Tactile MNIST and other benchmarks provide empirical evidence of its superiority and adaptability compared to less optimal baselines like random exploration.

Implications and Future Directions

This paper contributes substantial advancements in the development of adaptive tactile perception systems by showcasing TAP's robustness and generalizability, potentially ushering in a new paradigm of task-agnostic tactile frameworks. The implications for theoretical exploration include further insights into reinforcement learning's capacity to handle complex, partially observable environments effectively. On practical fronts, TAP's adaptability opens new avenues for robotic applications in unstructured environments where tactile feedback is critical, such as dexterous manipulation tasks and intricate material recognition.

For future research, an intriguing direction involves the real-world application through sim-to-real transfer, deploying TAP in tangible settings and expanding its scope to incorporate multiple sensorial modalities, such as vision fused with tactile input. Moreover, exploration into more efficient training methodologies, which could ameliorate the high sample efficiency demands inherent in current implementations, would greatly benefit practical scalability.

In conclusion, the TAP framework represents a meaningful stride in the landscape of tactile robotic perception, offering a flexible, robust approach poised to impact future exploration and manipulation strategies in complex environments.