Abstract-- Object insertion tasks are prone to failures under pose uncertainties and environmental variations, traditionally requiring manual finetuning or controller retraining. We present a novel approach for robust and resilient object insertion using a passively compliant soft wrist that enables safe contact absorption through large deformations, without high-frequency control or force sensing. Our method structures insertion as compliance-enabled contact formations, sequential contact states that progressively constrain degrees of freedom, and integrates automated failure recovery strategies. Our key insight is that wrist compliance permits safe, repeated recovery attempts; hence, we refer to it as compliance-enabled failure recovery. We employ a pre-trained vision-language model (VLM) that assesses each skill execution from terminal poses and images, identifies failure modes, and proposes recovery actions by selecting skills and updating goals. I. INTRODUCTION Peg-in-hole tasks have been widely studied but remain challenging due to their contact-rich nature and tight tolerances [1]. A central difficulty lies in handling uncertainties in part grasping and hole pose. Conventional methods address these uncertainties through precise pose estimation and rigid fixturing, enabling repetitive pick-and-place operations [2], but such approaches demand substantial engineering effort.

Perception in robot manipulation has been actively explored with the goal of advancing and integrating vision and touch for global and local feature extraction. However, it is difficult to perceive certain object internal states, and the integration of visual and haptic perception is not compact and is easily biased. We propose to address these limitations by developing an active acoustic sensing method for robot manipulation. Active acoustic sensing relies on the resonant properties of the object, which are related to its material, shape, internal structure, and contact interactions with the gripper and environment. The sensor consists of a vibration actuator paired with a piezo-electric microphone. The actuator generates a waveform, and the microphone tracks the waveform's propagation and distortion as it travels through the object. This paper presents the sensing principles, hardware design, simulation development, and evaluation of physical and simulated sensory data under different conditions as a proof-of-concept. This work aims to provide fundamentals on a useful tool for downstream robot manipulation tasks using active acoustic sensing, such as object recognition, grasping point estimation, object pose estimation, and external contact formation detection.

Humans can effortlessly perform very complex, dexterous manipulation tasks by reacting to sensor observations. In contrast, robots can not perform reactive manipulation and they mostly operate in open-loop while interacting with their environment. Consequently, the current manipulation algorithms either are inefficient in performance or can only work in highly structured environments. In this paper, we present closed-loop control of a complex manipulation task where a robot uses a tool to interact with objects. Manipulation using a tool leads to complex kinematics and contact constraints that need to be satisfied for generating feasible manipulation trajectories. We first present an open-loop controller design using Non-Linear Programming (NLP) that satisfies these constraints. In order to design a closed-loop controller, we present a pose estimator of objects and tools using tactile sensors. Using our tactile estimator, we design a closed-loop controller based on Model Predictive Control (MPC). The proposed algorithm is verified using a 6 DoF manipulator on tasks using a variety of objects and tools. We verify that our closed-loop controller can successfully perform tool manipulation under several unexpected contacts. Video summarizing this work and hardware experiments are found https://youtu.be/VsClK04qDhk.

Contact can be conceptualized as a set of constraints imposed on two bodies that are interacting with one another in some way. The nature of a contact, whether a point, line, or surface, dictates how these bodies are able to move with respect to one another given a force, and a set of contacts can provide either partial or full constraint on a body's motion. Decades of work have explored how to explicitly estimate the location of a contact and its dynamics, e.g., frictional properties, but investigated methods have been computationally expensive and there often exists significant uncertainty in the final calculation. This has affected further advancements in contact-rich tasks that are seemingly simple to humans, such as generalized peg-in-hole insertions. In this work, instead of explicitly estimating the individual contact dynamics between an object and its hole, we approach this problem by investigating compliance-enabled contact formations. More formally, contact formations are defined according to the constraints imposed on an object's available degrees-of-freedom. Rather than estimating individual contact positions, we abstract out this calculation to an implicit representation, allowing the robot to either acquire, maintain, or release constraints on the object during the insertion process, by monitoring forces enacted on the end effector through time. Using a compliant robot, our method is desirable in that we are able to complete industry-relevant insertion tasks of tolerances <0.25mm without prior knowledge of the exact hole location or its orientation. We showcase our method on more generalized insertion tasks, such as commercially available non-cylindrical objects and open world plug tasks.

We propose a method that simultaneously estimates and controls extrinsic contact with tactile feedback. The method enables challenging manipulation tasks that require controlling light forces and accurate motions in contact, such as balancing an unknown object on a thin rod standing upright. A factor graph-based framework fuses a sequence of tactile and kinematic measurements to estimate and control the interaction between gripper-object-environment, including the location and wrench at the extrinsic contact between the grasped object and the environment and the grasp wrench transferred from the gripper to the object. The same framework simultaneously plans the gripper motions that make it possible to estimate the state while satisfying regularizing control objectives to prevent slip, such as minimizing the grasp wrench and minimizing frictional force at the extrinsic contact. We show results with sub-millimeter contact localization error and good slip prevention even on slippery environments, for multiple contact formations (point, line, patch contact) and transitions between them. See supplementary video and results at https://sites.google.com/view/sim-tact.

Deformable object manipulation requires computationally efficient representations that are compatible with robotic sensing modalities. In this paper, we present VIRDO:an implicit, multi-modal, and continuous representation for deformable-elastic objects. VIRDO operates directly on visual (point cloud) and tactile (reaction forces) modalities and learns rich latent embeddings of contact locations and forces to predict object deformations subject to external contacts.Here, we demonstrate VIRDOs ability to: i) produce high-fidelity cross-modal reconstructions with dense unsupervised correspondences, ii) generalize to unseen contact formations,and iii) state-estimation with partial visio-tactile feedback