Naveen Kuppuswamy

Tao Chen, Eric Cousineau, Naveen Kuppuswamy et al.

Recent studies have made significant progress in addressing dexterous manipulation problems, particularly in in-hand object reorientation. However, there are few existing works that explore the potential utilization of developed dexterous manipulation controllers for downstream tasks. In this study, we focus on constrained dexterous manipulation for food peeling. Food peeling presents various constraints on the reorientation controller, such as the requirement for the hand to securely hold the object after reorientation for peeling. We propose a simple system for learning a reorientation controller that facilitates the subsequent peeling task. Videos are available at: https://taochenshh.github.io/projects/veg-peeling.

Jialiang Zhao, Naveen Kuppuswamy, Siyuan Feng et al.

Achieving robust dexterous manipulation in unstructured domestic environments remains a significant challenge in robotics. Even with state-of-the-art robot learning methods, haptic-oblivious control strategies (i.e. those relying only on external vision and/or proprioception) often fall short due to occlusions, visual complexities, and the need for precise contact interaction control. To address these limitations, we introduce PolyTouch, a novel robot finger that integrates camera-based tactile sensing, acoustic sensing, and peripheral visual sensing into a single design that is compact and durable. PolyTouch provides high-resolution tactile feedback across multiple temporal scales, which is essential for efficiently learning complex manipulation tasks. Experiments demonstrate an at least 20-fold increase in lifespan over commercial tactile sensors, with a design that is both easy to manufacture and scalable. We then use this multi-modal tactile feedback along with visuo-proprioceptive observations to synthesize a tactile-diffusion policy from human demonstrations; the resulting contact-aware control policy significantly outperforms haptic-oblivious policies in multiple contact-aware manipulation policies. This paper highlights how effectively integrating multi-modal contact sensing can hasten the development of effective contact-aware manipulation policies, paving the way for more reliable and versatile domestic robots. More information can be found at https://polytouch.alanz.info/

Zeyi Liu, Cheng Chi, Eric Cousineau et al.

Audio signals provide rich information for the robot interaction and object properties through contact. This information can surprisingly ease the learning of contact-rich robot manipulation skills, especially when the visual information alone is ambiguous or incomplete. However, the usage of audio data in robot manipulation has been constrained to teleoperated demonstrations collected by either attaching a microphone to the robot or object, which significantly limits its usage in robot learning pipelines. In this work, we introduce ManiWAV: an 'ear-in-hand' data collection device to collect in-the-wild human demonstrations with synchronous audio and visual feedback, and a corresponding policy interface to learn robot manipulation policy directly from the demonstrations. We demonstrate the capabilities of our system through four contact-rich manipulation tasks that require either passively sensing the contact events and modes, or actively sensing the object surface materials and states. In addition, we show that our system can generalize to unseen in-the-wild environments by learning from diverse in-the-wild human demonstrations.

Aimee Goncalves, Naveen Kuppuswamy, Andrew Beaulieu et al.

The manipulation of large objects and safe operation in the vicinity of humans are key capabilities of a general purpose domestic robotic assistant. We present the design of a soft, tactile-sensing humanoid upper-body robot and demonstrate whole-body rich-contact manipulation strategies for handling large objects. We demonstrate our hardware design philosophy for outfitting off-the-shelf hard robot arms and other components with soft tactile-sensing modules, including: (i) low-cost, cut-resistant, contact pressure localizing coverings for the arms, (ii) paws based on TRI's Soft-bubble sensors for the end effectors, and (iii) compliant force/geometry sensors for the coarse geometry sensing chest. We leverage the mechanical intelligence and tactile sensing of these modules to develop and demonstrate motion primitives for whole-body grasping. We evaluate the hardware's effectiveness in achieving grasps of varying strengths over a variety of large domestic objects. Our results demonstrate the importance of exploiting softness and tactile sensing in contact-rich manipulation strategies, as well as a path forward for whole-body force-controlled interactions with the world. (The supplemental video is available publicly at https://youtu.be/G8ZYgPRV5LY).

H. J. Terry Suh, Naveen Kuppuswamy, Tao Pang et al.

We propose the framework of Series Elastic End Effectors in 6D (SEED), which combines a spatially compliant element with visuotactile sensing to grasp and manipulate tools in the wild. Our framework generalizes the benefits of series elasticity to 6-dof, while providing an abstraction of control using visuotactile sensing. We propose an algorithm for relative pose estimation from visuotactile sensing, and a spatial hybrid force-position controller capable of achieving stable force interaction with the environment. We demonstrate the effectiveness of our framework on tools that require regulation of spatial forces. Video link: https://youtu.be/2-YuIfspDrk

Sihah Joonhigh, Naveen Kuppuswamy, Andrew Beaulieu et al.

While compliant grippers have become increasingly commonplace in robot manipulation, finding the right stiffness and geometry for grasping the widest variety of objects remains a key challenge. Adjusting both stiffness and gripper geometry on the fly may provide the versatility needed to manipulate the large range of objects found in domestic environments. We present a system for actively controlling the geometry (inflation level) and compliance of Soft-bubble grippers - air filled, highly compliant parallel gripper fingers incorporating visuotactile sensing. The proposed system enables large, controlled changes in gripper finger geometry and grasp stiffness, as well as simple in-hand manipulation. We also demonstrate, despite these changes, the continued viability of advanced perception capabilities such as dense geometry and shear force measurement - we present a straightforward extension of our previously presented approach for measuring shear induced displacements using the internal imaging sensor and taking into account pressure and geometry changes. We quantify the controlled variation of grasp-free geometry, grasp stiffness and contact patch geometry resulting from pressure regulation and we demonstrate new capabilities for the gripper in the home by grasping in constrained spaces, manipulating tools requiring lower and higher stiffness grasps, as well as contact patch modulation.

Rares Ambrus, Vitor Guizilini, Naveen Kuppuswamy et al.

Fluid-filled soft visuotactile sensors such as the Soft-bubbles alleviate key challenges for robust manipulation, as they enable reliable grasps along with the ability to obtain high-resolution sensory feedback on contact geometry and forces. Although they are simple in construction, their utility has been limited due to size constraints introduced by enclosed custom IR/depth imaging sensors to directly measure surface deformations. Towards mitigating this limitation, we investigate the application of state-of-the-art monocular depth estimation to infer dense internal (tactile) depth maps directly from the internal single small IR imaging sensor. Through real-world experiments, we show that deep networks typically used for long-range depth estimation (1-100m) can be effectively trained for precise predictions at a much shorter range (1-100mm) inside a mostly textureless deformable fluid-filled sensor. We propose a simple supervised learning process to train an object-agnostic network requiring less than 10 random poses in contact for less than 10 seconds for a small set of diverse objects (mug, wine glass, box, and fingers in our experiments). We show that our approach is sample-efficient, accurate, and generalizes across different objects and sensor configurations unseen at training time. Finally, we discuss the implications of our approach for the design of soft visuotactile sensors and grippers.

Naveen Kuppuswamy, Alex Alspach, Avinash Uttamchandani et al.

Manipulation in cluttered environments like homes requires stable grasps, precise placement and robustness against external contact. We present the Soft-Bubble gripper system with a highly compliant gripping surface and dense-geometry visuotactile sensing, capable of multiple kinds of tactile perception. We first present various mechanical design advances and a fabrication technique to deposit custom patterns to the internal surface of the sensor that enable tracking of shear-induced displacement of the manipuland. The depth maps output by the internal imaging sensor are used in an in-hand proximity pose estimation framework -- the method better captures distances to corners or edges on the manipuland geometry. We also extend our previous work on tactile classification and integrate the system within a robust manipulation pipeline for cluttered home environments. The capabilities of the proposed system are demonstrated through robust execution multiple real-world manipulation tasks. A video of the system in action can be found here: [https://youtu.be/G_wBsbQyBfc].

Alejandro M. Castro, Ante Qu, Naveen Kuppuswamy et al.

Multibody simulation with frictional contact has been a challenging subject of research for the past thirty years. Rigid-body assumptions are commonly used to approximate the physics of contact, and together with Coulomb friction, lead to challenging-to-solve nonlinear complementarity problems (NCP). On the other hand, robot grippers often introduce significant compliance. Compliant contact, combined with regularized friction, can be modeled entirely with ODEs, avoiding NCP solves. Unfortunately, regularized friction introduces high-frequency stiff dynamics and even implicit methods struggle with these systems, especially during slip-stick transitions. To improve the performance of implicit integration for these systems we introduce a Transition-Aware Line Search (TALS), which greatly improves the convergence of the Newton-Raphson iterations performed by implicit integrators. We find that TALS works best with semi-implicit integration, but that the explicit treatment of normal compliance can be problematic. To address this, we develop a Transition-Aware Modified Semi-Implicit (TAMSI) integrator that has similar computational cost to semi-implicit methods but implicitly couples compliant contact forces, leading to a more robust method. We evaluate the robustness, accuracy and performance of TAMSI and demonstrate our approach alongside relevant sim-to-real manipulation tasks.

Alex Alspach, Kunimatsu Hashimoto, Naveen Kuppuswamy et al.

Incorporating effective tactile sensing and mechanical compliance is key towards enabling robust and safe operation of robots in unknown, uncertain and cluttered environments. Towards realizing this goal, we present a lightweight, easy-to-build, highly compliant dense geometry sensor and end effector that comprises an inflated latex membrane with a depth sensor behind it. We present the motivations and the hardware design for this Soft-bubble and demonstrate its capabilities through example tasks including tactile-object classification, pose estimation and tracking, and nonprehensile object manipulation. We also present initial experiments to show the importance of high-resolution geometry sensing for tactile tasks and discuss applications in robust manipulation.