Proprioceptive Learning with Soft Polyhedral Networks

Proprioception is the "sixth sense" that detects limb postures with motor neurons. It requires a natural integration between the musculoskeletal systems and sensory receptors, which is challenging among modern robots that aim for lightweight, adaptive, and sensitive designs at a low cost. Here, we present the Soft Polyhedral Network with an embedded vision for physical interactions, capable of adaptive kinesthesia and viscoelastic proprioception by learning kinetic features. This design enables passive adaptations to omni-directional interactions, visually captured by a miniature high-speed motion tracking system embedded inside for proprioceptive learning. The results show that the soft network can infer real-time 6D forces and torques with accuracies of 0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also incorporate viscoelasticity in proprioception during static adaptation by adding a creep and relaxation modifier to refine the predicted results. The proposed soft network combines simplicity in design, omni-adaptation, and proprioceptive sensing with high accuracy, making it a versatile solution for robotics at a low cost with more than 1 million use cycles for tasks such as sensitive and competitive grasping, and touch-based geometry reconstruction. This study offers new insights into vision-based proprioception for soft robots in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International Journal of Robotics Research for revie

The Architecture of Soft Machines

This thesis speculates about the possibility of softening architecture through machines. In deviating from traditional mechanical conceptions of machines based on autonomous, functional and purely operational notions, the thesis proposes to conceive of machines as corporeal media in co-constituting relationships with human bodies. As machines become corporeal (robots) and human bodies take on qualities of machines (cyborgs) the thesis investigates their relations to architecture through readings of William S. Burroughs’ proto-cyborgian novel The Soft Machine (1961) and Georges Teyssot’s essay ‘Hybrid Architecture: An Environment for the Prosthetic Body’ (2005) arguing for a revision of architecture’s anthropocentric mandate in favour of technologically co-constituting body ideas. The conceptual shift in man-machine relations is also demonstrated by discussion of two installations shown at the Venice Biennale, Daniel Libeskind’s mechanical Three Lessons in Architecture (1985) and Philip Beesely’s responsive Hylozoic Ground (2010). As the purely mechanical model has been superseded by a model that incorporates digital sensing and embedded actuation, as well as soft and compliant materiality, the promise of softer, more sensitive and corporeal conceptions of technology shines onto architecture. Following Nicholas Negroponte’s ambition for a ‘humanism through machines,’ stated in his groundbreaking work, Soft Architecture Machines (1975), and inspired by recent developments in the emerging field of soft robotics, I have developed a series of practical design experiments, ranging from soft mechanical hybrids to soft machines made entirely from silicone and actuated by embedded pneumatics, to speculate about architectural environments capable of interacting with humans. In a radical departure from traditional mechanical conceptions based on modalities of assembly, the design of these types of soft machines is derived from soft organisms such as molluscs (octopi, snails, jellyfish) in order to infuse them with notions of flexibility, compliance, sensitivity, passive dynamics and spatial variability. Challenging architecture’s alliance with notions of permanence and monumentality, the thesis finally formulates a critique of static typologisation of space with walls, floors, columns or windows. In proposing an embodied architecture the thesis concludes by speculating about architecture as a capacitated, sensitive and sensual body informed by reciprocal conditioning of constituent systems, materials, morphologies and behaviours

Study of Kinesthetic Feedback Control for Compliant Proprioceptive Touch for Soft Robotic Finger-like Actuators

The compliant nature of soft robotic components lends itself well to manipulation and contact-rich tasks. The soft structures naturally form around objects where there could be uncertainty in shape or orientation and are inherently safer for fragile objects and humans. However, this compliancy makes the robot’s movement less constrained and less predictable making it difficult to control the position of the soft robotic manipulator without new types of sensing. To address these drawbacks, the combination of curvature, inflation, and contact sensors are added to give the finger the unique capability of somatosensory abilities, enhancing how it interacts with objects and its controllability. In this dissertation, we present the application of various approaches that provide a sense of touch for compliant soft robotic fingers. We rethink and validate the role that sensory feedback can play in the control of soft finger-like actuators with proprioceptive sensing capabilities. In our method of touch detection, instead of using the sensory feedback to control precisely the position of a soft finger, we use the disagreement between the controlled curvature sensor measurement and its reference signal to detect the contact between the soft finger and an object. We first consider the case of a static characteristic relation between the inflation pressure and sensor resistance. A control architecture is presented utilizing both the curvature and force sensors with the aim of providing a firm touch of a soft somatosensitive actuator with an object. The first component of the architecture, a reference tracking curvature controller, sets the finger in motion, which becomes blocked if an object is in its path. The result of such an event is that the finger bending is constrained, and the tracking error of the curvature controller increases. Once the error exceeds a predetermined threshold value, there is a switch from the reference tracking curvature controller to the second component, a force controller, which maintains the finger in contact with the object for a certain pressure using the force sensor measurement. We next consider a method for a kinesthetic touch approach for object detection that does not require the force sensor; therefore, it overcomes the necessity for the co-location between the point of contact and the corresponding sensor. The control architecture uses only the finger’s proprioceptive curvature sensor to detect contact with an object and maintain contact by switching to a different reference to hold at a constant curvature. Lastly, we focus on a method for identifying the dynamic finger curvature model to improve the closed-loop control. The proposed method addresses environmental variations as well as variations in material or human factors during the fabrication process that can have an effect on the finger dynamics. The approach uses the reference tracking error as a measure of the finger stress resulting from a contact with an object. The approach for contact detection has been tested in various tasks, including keeping the finger in a firm touch with an object, detecting the object edges and visualizing the operating space based on the sense of touch. These tasks demonstrate that the error signal contains robust information regarding the finger’s sense of touch and how it interacts with the environment. Findings are demonstrated across both a proxy of a soft finger, a simulated compliant multi-link actuator with flexible joints, and a real soft robotic finger

Study of Kinesthetic Feedback Control for Compliant Proprioceptive Touch for Soft Robotic Finger-like Actuators

The compliant nature of soft robotic components lends itself well to manipulation and contact-rich tasks. The soft structures naturally form around objects where there could be uncertainty in shape or orientation and are inherently safer for fragile objects and humans. However, this compliancy makes the robot’s movement less constrained and less predictable making it difficult to control the position of the soft robotic manipulator without new types of sensing. To address these drawbacks, the combination of curvature, inflation, and contact sensors are added to give the finger the unique capability of somatosensory abilities, enhancing how it interacts with objects and its controllability. In this dissertation, we present the application of various approaches that provide a sense of touch for compliant soft robotic fingers. We rethink and validate the role that sensory feedback can play in the control of soft finger-like actuators with proprioceptive sensing capabilities. In our method of touch detection, instead of using the sensory feedback to control precisely the position of a soft finger, we use the disagreement between the controlled curvature sensor measurement and its reference signal to detect the contact between the soft finger and an object. We first consider the case of a static characteristic relation between the inflation pressure and sensor resistance. A control architecture is presented utilizing both the curvature and force sensors with the aim of providing a firm touch of a soft somatosensitive actuator with an object. The first component of the architecture, a reference tracking curvature controller, sets the finger in motion, which becomes blocked if an object is in its path. The result of such an event is that the finger bending is constrained, and the tracking error of the curvature controller increases. Once the error exceeds a predetermined threshold value, there is a switch from the reference tracking curvature controller to the second component, a force controller, which maintains the finger in contact with the object for a certain pressure using the force sensor measurement. We next consider a method for a kinesthetic touch approach for object detection that does not require the force sensor; therefore, it overcomes the necessity for the co-location between the point of contact and the corresponding sensor. The control architecture uses only the finger’s proprioceptive curvature sensor to detect contact with an object and maintain contact by switching to a different reference to hold at a constant curvature. Lastly, we focus on a method for identifying the dynamic finger curvature model to improve the closed-loop control. The proposed method addresses environmental variations as well as variations in material or human factors during the fabrication process that can have an effect on the finger dynamics. The approach uses the reference tracking error as a measure of the finger stress resulting from a contact with an object. The approach for contact detection has been tested in various tasks, including keeping the finger in a firm touch with an object, detecting the object edges and visualizing the operating space based on the sense of touch. These tasks demonstrate that the error signal contains robust information regarding the finger’s sense of touch and how it interacts with the environment. Findings are demonstrated across both a proxy of a soft finger, a simulated compliant multi-link actuator with flexible joints, and a real soft robotic finger