Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning

• URL: http://arxiv.org/abs/2204.00117v1
• Date: Thu, 31 Mar 2022 21:59:08 GMT
• Title: Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning
• Authors: Leszek Pecyna, Siyuan Dong, Shan Luo
• Abstract summary: We study the problem of using vision and tactile inputs together to complete the task of following deformable linear objects. We create a Reinforcement Learning agent using different sensing modalities and investigate how its behaviour can be boosted. Our experiments show that the use of both vision and tactile inputs, together with proprioception, allows the agent to complete the task in up to 92% of cases.
• Score: 15.758583731036007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manipulation of deformable objects is a challenging task for a robot. It will be problematic to use a single sensory input to track the behaviour of such objects: vision can be subjected to occlusions, whereas tactile inputs cannot capture the global information that is useful for the task. In this paper, we study the problem of using vision and tactile inputs together to complete the task of following deformable linear objects, for the first time. We create a Reinforcement Learning agent using different sensing modalities and investigate how its behaviour can be boosted using visual-tactile fusion, compared to using a single sensing modality. To this end, we developed a benchmark in simulation for manipulating the deformable linear objects using multimodal sensing inputs. The policy of the agent uses distilled information, e.g., the pose of the object in both visual and tactile perspectives, instead of the raw sensing signals, so that it can be directly transferred to real environments. In this way, we disentangle the perception system and the learned control policy. Our extensive experiments show that the use of both vision and tactile inputs, together with proprioception, allows the agent to complete the task in up to 92% of cases, compared to 77% when only one of the signals is given. Our results can provide valuable insights for the future design of tactile sensors and for deformable objects manipulation.

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