59 research outputs found
Model-free Soft-Structure Reconstruction for Proprioception using Tactile Arrays
Continuum body structures provide unique opportunities for soft robotics, with the infinite degrees of freedom
enabling unconstrained and highly adaptive exploration and manipulation. However, the infinite degrees of freedom of continuum
bodies makes sensing (both intrinsically and extrinsically) challenging. To address this, in this paper we propose a model-free
method for sensorizing tentacle-like continuum soft-structures
using an array of spatially arranged capacitive tactile sensors.
By using visual tracking, the relationship between the tactile
response and the 3D shape of the continuum soft-structure can be
learned. A data set of 15000 random soft-body postures was used,
with recorded camera-tracked positions logged synchronously to
the tactile sensor responses. This was used to train a neural
network which can predict posture. We show it is possible to
achieve proprioceptive awareness over all three axis of motion
in space, reconstructing the body structure and inferring the
soft body head’s pose with an average accuracy of ≈ 1mm in
comparison to the visual tracked counterpart. To demonstrate
the capabilities of the system, we perform random exploration
of environments limiting the work-space of the sensorized robot.
We find the method capable to autonomously reconstruct the
reachable morphology of the environment without the need of
external sensing units.This work was funded by the UK Agriculture and Horticulture Development
Board (CP 172) and Physical Sciences Research Council (EPSRC) MOTION
grant [EP/N03211X/2
Active touch for robust perception under position uncertainty
In this paper, we propose that active perception will help attain autonomous robotics in unstructured environments by giving robust perception. We test this claim with a biomimetic fingertip that senses surface texture under a range of contact depths. We compare the performance of passive Bayesian perception with a novel approach for active perception that includes a sensorimotor loop for controlling sensor position. Passive perception at a single depth gave poor results, with just 0.2mm uncertainty impairing performance. Extending passive perception over a range of depths gave non-robust performance. Only active perception could give robust, accurate performance, with the sensorimotor feedback compensating the position uncertainty. We expect that these results will extend to other stimuli, so that active perception will offer a general approach to robust perception in unstructured environments
Active haptic shape recognition by intrinsic motivation with a robot hand
In this paper, we present an intrinsic motivation approach applied to haptics in robotics for tactile object exploration and recognition. Here, touch is used as the sensation process for contact detection, whilst proprioceptive information is used for the perception process. First, a probabilistic method is employed to reduce uncertainty present in tactile measurements. Second, the object exploration process is actively controlled by intelligently moving the robot hand towards interesting locations. The active behaviour performed with the robotic hand is achieved by an intrinsic motivation approach, which permitted to improve the accuracy for object recognition over the results obtained by a fixed sequence of exploration movements. The proposed method was validated in a simulated environment with a Monte Carlo method, whilst for the real environment a three-fingered robotic hand and various object shapes were employed. The results demonstrate that our method is robust and suitable for haptic perception in autonomous robotics
Adaptive perception: learning from sensory predictions to extract object shape with a biomimetic fingertip
In this work, we present an adaptive perception method to improve the performance in accuracy and speed of a tactile exploration task. This work extends our previous studies on sensorimotor control strategies for active tactile perception in robotics. First, we present the active Bayesian perception method to actively reposition a robot to accumulate evidence from better locations to reduce uncertainty. Second, we describe the adaptive perception method that, based on a forward model and a predicted information gain approach, allows to the robot to analyse `what would have happened' if a different decision `would have been made' at previous decision time. This approach permits to adapt the active Bayesian perception process to improve the performance in accuracy and reaction time of an exploration task. Our methods are validated with a contour following exploratory procedure with a touch sensor. The results show that the adaptive perception method allows the robot to make sensory predictions and autonomously adapt, improving the performance of the exploration task
Embodied hyperacuity from Bayesian perception: Shape and position discrimination with an iCub fingertip sensor
Recent advances in modeling animal perception has motivated an approach of Bayesian perception applied to biomimetic robots. This study presents an initial application of Bayesian perception on an iCub fingertip sensor mounted on a dedicated positioning robot. We systematically probed the test system with five cylindrical stimuli offset by a range of positions relative to the fingertip. Testing the real-time speed and accuracy of shape and position discrimination, we achieved sub-millimeter accuracy with just a few taps. This result is apparently the first explicit demonstration of perceptual hyperacuity in robot touch, in that object positions are perceived more accurately than the taxel spacing. We also found substantial performance gains when the fingertip can reposition itself to avoid poor perceptual locations, which indicates that improved robot perception could mimic active perception in animals
Active Bayesian perception and reinforcement learning
In a series of papers, we have formalized an active Bayesian perception approach for robotics based on recent progress in understanding animal perception. However, an issue for applied robot perception is how to tune this method to a task, using: (i) a belief threshold that adjusts the speed-accuracy tradeoff; and (ii) an active control strategy for relocating the sensor e.g. to a preset fixation point. Here we propose that these two variables should be learnt by reinforcement from a reward signal evaluating the decision outcome. We test this claim with a biomimetic fingertip that senses surface curvature under uncertainty about contact location. Appropriate formulation of the problem allows use of multi-armed bandit methods to optimize the threshold and fixation point of the active perception. In consequence, the system learns to balance speed versus accuracy and sets the fixation point to optimize both quantities. Although we consider one example in robot touch, we expect that the underlying principles have general applicability
Soft Robotic Link with Controllable Transparency for Vision-based Tactile and Proximity Sensing
Robots have been brought to work close to humans in many scenarios. For
coexistence and collaboration, robots should be safe and pleasant for humans to
interact with. To this end, the robots could be both physically soft with
multimodal sensing/perception, so that the robots could have better awareness
of the surrounding environment, as well as to respond properly to humans'
action/intention. This paper introduces a novel soft robotic link, named
ProTac, that possesses multiple sensing modes: tactile and proximity sensing,
based on computer vision and a functional material. These modalities come from
a layered structure of a soft transparent silicon skin, a polymer dispersed
liquid crystal (PDLC) film, and reflective markers. Here, the PDLC film can
switch actively between the opaque and the transparent state, from which the
tactile sensing and proximity sensing can be obtained by using cameras solely
built inside the ProTac link. In this paper, inference algorithms for tactile
proximity perception are introduced. Evaluation results of two sensing
modalities demonstrated that, with a simple activation strategy, ProTac link
could effectively perceive useful information from both approaching and
in-contact obstacles. The proposed sensing device is expected to bring in
ultimate solutions for design of robots with softness, whole-body and
multimodal sensing, and safety control strategies.Comment: Submitted to RoboSoft 2023 for review. Final content subjected to
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