AcTExplore: Active Tactile Exploration of Unknown Objects
AcTExplore employs reinforcement learning for active tactile exploration, achieving 95.97% IoU coverage on unseen objects with limited steps.
Key Findings
Methodology
This approach formulates the exploration as a Partially Observable Markov Decision Process (POMDP), integrating deep reinforcement learning with multimodal state representations, including temporal sequences of tactile data. The action space comprises 6-DOF movements, optimized via a modified PPO algorithm. The reward combines contact area maximization and curiosity-driven exploration bonuses, encouraging the agent to cover the object surface efficiently. Training is conducted in simulation with primitive shapes (sphere, cube), then tested on unseen YCB objects, achieving an average IoU of 95.97%. The system incorporates UCB-based exploration bonuses and short-term memory to avoid revisiting explored regions.
Key Results
- On unseen YCB objects, the method achieved an average IoU of 95.97%, significantly surpassing passive or single-sensor approaches. The incorporation of temporal sequence representations (TTA, TTS) improved exploration efficiency and shape accuracy. Ablation studies confirmed that combining time-aware state encoding with curiosity-driven rewards enhances coverage and reduces exploration steps. The trained policy transferred successfully from simulation to real-world robotic platform (UR10+DIGIT), demonstrating robustness and practical applicability.
- Comparative experiments showed that TTS and AMB reward schemes outperform alternatives, with IoU consistently above 90%. The approach converged within 200K steps in simulation, with real-world tests confirming similar performance. The results indicate that temporal information and reward design are critical for effective exploration. The method generalizes well to complex geometries and different materials, outperforming traditional passive reconstruction and single-modality strategies.
- This work introduces a novel active tactile exploration framework that combines reinforcement learning, multimodal temporal sensing, and curiosity incentives. It addresses key challenges of limited tactile coverage and path planning in unknown environments, enabling autonomous, efficient 3D shape reconstruction. The approach opens new avenues for robotic perception, especially in scenarios where vision is obstructed or unavailable, and sets a foundation for future multi-sensor, multi-object, dynamic environment exploration.
Significance
This research advances robotic tactile perception by enabling autonomous, efficient surface exploration and shape reconstruction in unknown environments. It overcomes traditional limitations of passive sensing and sparse coverage, providing a scalable solution for real-world applications such as industrial inspection, medical robotics, and autonomous navigation. The integration of reinforcement learning with multimodal temporal sensing enhances the robot’s decision-making capabilities, pushing the frontier of self-guided perception systems. The successful sim-to-real transfer demonstrates practical viability, promising broader industrial and service robotics deployment, and fostering more intelligent autonomous systems capable of understanding complex environments through active sensing.
Technical Contribution
The study introduces a novel reinforcement learning framework that integrates multimodal temporal tactile data for active exploration. It innovates by designing a 6-DOF action space with curiosity-driven exploration bonuses, and a state representation that encodes short-term memory via temporal stacking and averaging. The reward function combines contact area maximization with an exploration bonus based on visit counts, effectively balancing exploitation and exploration. The approach employs a modified PPO algorithm with variance reduction techniques, ensuring sample efficiency and stability. The system’s ability to transfer from simulation to real-world robots without fine-tuning marks a significant engineering breakthrough, enabling practical deployment of active tactile exploration in complex scenarios.
Novelty
This work is the first to combine deep reinforcement learning with multimodal temporal tactile sensing for active 3D exploration of unknown objects. Unlike previous passive or single-sensor approaches, it actively guides the sensor to maximize surface coverage within limited steps, leveraging curiosity-driven rewards and short-term memory. The integration of a 6-DOF action space with a sophisticated state encoding and exploration bonus mechanism represents a fundamental innovation, enabling autonomous, efficient shape reconstruction in complex, real-world environments. This approach sets a new standard for active sensing in robotics.
Limitations
- The current framework assumes a fixed, rigid object and a sensor mounted on a robot arm, limiting applicability to dynamic or multi-object scenarios. The reliance on simulation data for training may pose challenges in highly cluttered or unstructured environments. The action space, while flexible, may require further refinement for collision avoidance and path planning in real-time applications. Additionally, the method’s performance depends on the fidelity of tactile simulation, which might differ from real sensor noise and deformation behaviors. Computational costs for training and inference could also limit real-time deployment in resource-constrained systems.
Future Work
未来将扩展多模态感知融合（如视觉与触觉结合），以增强环境适应性。研究动态场景中的连续探索策略，提升多物体和复杂环境中的自主感知能力。优化路径规划和碰撞避免机制，增强系统的鲁棒性。探索端到端学习框架，结合自主决策与动作执行，推动机器人自主感知和操作的智能化发展。还将关注多传感器融合、多目标任务和动态环境中的自主探索，推动机器人感知能力的全面提升。
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
想象你在黑暗的房间里找你的手机，你用手不停地摸索，试图找到它的位置。你会用手指去触摸每个角落，感觉到手机的形状和位置，然后记住它的地方。机器人也是这样，它用一种特殊的“手”——触觉传感器，主动去摸未知的物体。它会不断移动“手”的位置，触摸不同的地方，逐渐拼凑出物体的形状。就像你在黑暗中用手摸索一样，机器人通过不断“触碰”来了解环境。它会学习哪些动作能让它摸得更快、更全，最后能在很少的尝试中，把整个物体的样子都“摸”出来。这种主动摸索的方法，比被动等待信息更聪明，也更快。未来，这样的机器人可以帮你在黑暗中找东西，或者在复杂环境中工作，就像你用手去“感受”世界一样有趣！
ELI14 Explained like you're 14
想象你在黑暗的房间里找东西，你用手不停地摸索，试图找到它。你会用手指去碰每个角落，感觉到它的形状和位置，然后记住它在哪里。机器人也是这样，它用一种特别的“手”——触觉传感器，主动去摸未知的东西。它会不断移动“手”的位置，触摸不同的地方，慢慢拼出物体的样子。就像你在黑暗中用手摸索一样，机器人通过不断“触碰”来了解环境。它会学习哪些动作能让它摸得更快、更全，最后能在很少的尝试中，把整个物体的样子都“摸”出来。这种主动摸索的方法，比被动等待信息更聪明，也更快。未来，这样的机器人可以帮你在黑暗中找东西，或者在复杂的地方工作，就像你用手去“感受”世界一样有趣！
Abstract
Tactile exploration plays a crucial role in understanding object structures for fundamental robotics tasks such as grasping and manipulation. However, efficiently exploring such objects using tactile sensors is challenging, primarily due to the large-scale unknown environments and limited sensing coverage of these sensors. To this end, we present AcTExplore, an active tactile exploration method driven by reinforcement learning for object reconstruction at scales that automatically explores the object surfaces in a limited number of steps. Through sufficient exploration, our algorithm incrementally collects tactile data and reconstructs 3D shapes of the objects as well, which can serve as a representation for higher-level downstream tasks. Our method achieves an average of 95.97% IoU coverage on unseen YCB objects while just being trained on primitive shapes. Project Webpage: https://prg.cs.umd.edu/AcTExplore