LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping

Grasping is a crucial task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects under various conditions and with differing physical properties. In this paper, we introduce LeTac-MPC, a learning-based model predictive control (MPC) for tactile-reactive grasping. Our approach enables the gripper grasp objects with different physical properties on dynamic and force-interactive tasks. We utilize a vision-based tactile sensor, GelSight, which is capable of perceiving high-resolution tactile feedback that contains the information of physical properties and states of the grasped object. LeTac-MPC incorporates a differentiable MPC layer designed to model the embeddings extracted by a neural network (NN) from tactile feedback. This design facilitates convergent and robust grasping control at a frequency of 25 Hz. We propose a fully automated data collection pipeline and collect a dataset only using standardized blocks with different physical properties. However, our trained controller can generalize to daily objects with different sizes, shapes, materials, and textures. Experimental results demonstrate the effectiveness and robustness of the proposed approach. We compare LeTac-MPC with two purely model-based tactile-reactive controllers (MPC and PD) and open-loop grasping. Our results show that LeTac-MPC has the best performance on dynamic and force-interactive tasks and the best generalization ability. We release our code and dataset at https://github.com/ZhengtongXu/LeTac-MPC.

LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization

This paper introduces LeTO, a method for learning constrained visuomotor policy via differentiable trajectory optimization. Our approach uniquely integrates a differentiable optimization layer into the neural network. By formulating the optimization layer as a trajectory optimization problem, we enable the model to end-to-end generate actions in a safe and controlled fashion without extra modules. Our method allows for the introduction of constraints information during the training process, thereby balancing the training objectives of satisfying constraints, smoothing the trajectories, and minimizing errors with demonstrations. This "gray box" method marries the optimization-based safety and interpretability with the powerful representational abilities of neural networks. We quantitatively evaluate LeTO in simulation and on the real robot. In simulation, LeTO achieves a success rate comparable to state-of-the-art imitation learning methods, but the generated trajectories are of less uncertainty, higher quality, and smoother. In real-world experiments, we deployed LeTO to handle constraints-critical tasks. The results show the effectiveness of LeTO comparing with state-of-the-art imitation learning approaches. We release our code at https://github.com/ZhengtongXu/LeTO.

Robot Tape Manipulation for 3D Printing

3D printing has enabled various applications using different forms of materials, such as filaments, sheets, and inks. Typically, during 3D printing, feedstocks are transformed into discrete building blocks and placed or deposited in a designated location similar to the manipulation and assembly of discrete objects. However, 3D printing of continuous and flexible tape (with the geometry between filaments and sheets) without breaking or transformation remains underexplored and challenging. Here, we report the design and implementation of a customized end-effector, i.e., tape print module (TPM), to realize robot tape manipulation for 3D printing by leveraging the tension formed on the tape between two endpoints. We showcase the feasibility of manufacturing representative 2D and 3D structures while utilizing conductive copper tape for various electronic applications, such as circuits and sensors. We believe this manipulation strategy could unlock the potential of other tape materials for manufacturing, including packaging tape and carbon fiber prepreg tape, and inspire new mechanisms for robot manipulation, 3D printing, and packaging.

Visuotactile Affordances for Cloth Manipulation with Local Control

Cloth in the real world is often crumpled, self-occluded, or folded in on itself such that key regions, such as corners, are not directly graspable, making manipulation difficult. We propose a system that leverages visual and tactile perception to unfold the cloth via grasping and sliding on edges. By doing so, the robot is able to grasp two adjacent corners, enabling subsequent manipulation tasks like folding or hanging. As components of this system, we develop tactile perception networks that classify whether an edge is grasped and estimate the pose of the edge. We use the edge classification network to supervise a visuotactile edge grasp affordance network that can grasp edges with a 90% success rate. Once an edge is grasped, we demonstrate that the robot can slide along the cloth to the adjacent corner using tactile pose estimation/control in real time. See http://nehasunil.com/visuotactile/visuotactile.html for videos.

Learning Generalizable Vision-Tactile Robotic Grasping Strategy for Deformable Objects via Transformer

Reliable robotic grasping, especially with deformable objects such as fruits, remains a challenging task due to underactuated contact interactions with a gripper, unknown object dynamics and geometries. In this study, we propose a Transformer-based robotic grasping framework for rigid grippers that leverage tactile and visual information for safe object grasping. Specifically, the Transformer models learn physical feature embeddings with sensor feedback through performing two pre-defined explorative actions (pinching and sliding) and predict a grasping outcome through a multilayer perceptron (MLP) with a given grasping strength. Using these predictions, the gripper predicts a safe grasping strength via inference. Compared with convolutional recurrent networks (CNN), the Transformer models can capture the long-term dependencies across the image sequences and process spatial-temporal features simultaneously. We first benchmark the Transformer models on a public dataset for slip detection. Following that, we show that the Transformer models outperform a CNN+LSTM model in terms of grasping accuracy and computational efficiency. We also collect our fruit grasping dataset and conduct online grasping experiments using the proposed framework for both seen and unseen fruits. Our codes and dataset are public on GitHub.

GelSight Wedge: Measuring High-Resolution 3D Contact Geometry with a Compact Robot Finger

Vision-based tactile sensors have the potential to provide important contact geometry to localize the objective with visual occlusion. However, it is challenging to measure high-resolution 3D contact geometry for a compact robot finger, to simultaneously meet optical and mechanical constraints. In this work, we present the GelSight Wedge sensor, which is optimized to have a compact shape for robot fingers, while achieving high-resolution 3D reconstruction. We evaluate the 3D reconstruction under different lighting configurations, and extend the method from 3 lights to 1 or 2 lights. We demonstrate the flexibility of the design by shrinking the sensor to the size of a human finger for fine manipulation tasks. We also show the effectiveness and potential of the reconstructed 3D geometry for pose tracking in the 3D space.

Cable Manipulation with a Tactile-Reactive Gripper

Manipulation of flexible cables is relevant to both industrial and household environments. In this paper, we develop a perception and control framework to enable robots to accomplish the task of following a cable. We rely on a vision-based tactile sensor, GelSight, to estimate the pose of the cable in the grip as well as the friction forces during cable sliding. We decompose the behavior of cable following into two tactile-based controllers: 1) Cable grip controller, where a PD controller combined with a leaky integrator are responsible for regulating the gripping force to maintain the frictional sliding forces close to a suitable value; and 2) Cable pose controller, where an LQR controller based on a learned linear model of the cable sliding dynamics is in charge of keeping the cable centered and aligned on the fingertips to prevent the cable from falling. This behavior is enabled by a designed reactive gripper with force and position control capabilities fitted with GelSight-based high resolution tactile sensors. With the proposed framework, we show that the robot can follow one meter of cable in a random configuration from beginning to end within 2-3 hand regrasps. We further demonstrate that the closed-loop system adapts to cables with different materials and thicknesses, moving at different target velocities.

Exoskeleton-covered soft finger with vision-based proprioception and exteroception

Soft robots offer significant advantages in adaptability, safety, and dexterity compared to conventional rigid-body robots. However, it is challenging to equip soft robots with accurate proprioception and exteroception due to their high flexibility and elasticity. In this work, we develop a novel exoskeleton-covered soft finger with embedded cameras and deep learning methods that enable high-resolution proprioceptive sensing and rich tactile sensing. To do so, we design features along the axial direction of the finger, which enable high-resolution proprioceptive sensing, and incorporate a reflective ink coating on the surface of the finger to enable rich tactile sensing. We design a highly underactuated exoskeleton with a tendon-driven mechanism to actuate the finger. Finally, we assemble 2 of the fingers together to form a robotic gripper and successfully perform a bar stock classification task, which requires both shape and tactile information. We train neural networks for proprioception and shape (box versus cylinder) classification using data from the embedded sensors. The proprioception CNN had over 99\% accuracy on our testing set (all six joint angles were within 1$^\circ$ of error) and had an average accumulative distance error of 0.77 mm during live testing, which is better than human finger proprioception. These proposed techniques offer soft robots the high-level ability to simultaneously perceive their proprioceptive state and peripheral environment, providing potential solutions for soft robots to solve everyday manipulation tasks. We believe the methods developed in this work can be widely applied to different designs and applications.