POE: Acoustic Soft Robotic Proprioception for Omnidirectional End-effectors

Soft robotic shape estimation and proprioception are challenging because of soft robot's complex deformation behaviors and infinite degrees of freedom. A soft robot's continuously deforming body makes it difficult to integrate rigid sensors and to reliably estimate its shape. In this work, we present Proprioceptive Omnidirectional End-effector (POE), which has six embedded microphones across the tendon-driven soft robot's surface. We first introduce novel applications of previously proposed 3D reconstruction methods to acoustic signals from the microphones for soft robot shape proprioception. To improve the proprioception pipeline's training efficiency and model prediction consistency, we present POE-M. POE-M first predicts key point positions from the acoustic signal observations with the embedded microphone array. Then we utilize an energy-minimization method to reconstruct a physically admissible high-resolution mesh of POE given the estimated key points. We evaluate the mesh reconstruction module with simulated data and the full POE-M pipeline with real-world experiments. We demonstrate that POE-M's explicit guidance of the key points during the mesh reconstruction process provides robustness and stability to the pipeline with ablation studies. POE-M reduced the maximum Chamfer distance error by 23.10 % compared to the state-of-the-art end-to-end soft robot proprioception models and achieved 4.91 mm average Chamfer distance error during evaluation.

MD-Splatting: Learning Metric Deformation from 4D Gaussians in Highly Deformable Scenes

Accurate 3D tracking in highly deformable scenes with occlusions and shadows can facilitate new applications in robotics, augmented reality, and generative AI. However, tracking under these conditions is extremely challenging due to the ambiguity that arises with large deformations, shadows, and occlusions. We introduce MD-Splatting, an approach for simultaneous 3D tracking and novel view synthesis, using video captures of a dynamic scene from various camera poses. MD-Splatting builds on recent advances in Gaussian splatting, a method that learns the properties of a large number of Gaussians for state-of-the-art and fast novel view synthesis. MD-Splatting learns a deformation function to project a set of Gaussians with non-metric, thus canonical, properties into metric space. The deformation function uses a neural-voxel encoding and a multilayer perceptron (MLP) to infer Gaussian position, rotation, and a shadow scalar. We enforce physics-inspired regularization terms based on local rigidity, conservation of momentum, and isometry, which leads to trajectories with smaller trajectory errors. MD-Splatting achieves high-quality 3D tracking on highly deformable scenes with shadows and occlusions. Compared to state-of-the-art, we improve 3D tracking by an average of 23.9 %, while simultaneously achieving high-quality novel view synthesis. With sufficient texture such as in scene 6, MD-Splatting achieves a median tracking error of 3.39 mm on a cloth of 1 x 1 meters in size. Project website: https://md-splatting.github.io/.

FogROS2-Sky: Optimizing Latency and Cost for Multi-Cloud Robot Applications

This paper studies the cost-performance tradeoffs in cloud robotics with heterogeneous cloud service providers, which have complex pricing models and varying application requirements. We present FogROS2-Sky, a cost-efficient open source robotics platform that offloads unmodified ROS2 applications to multiple cloud providers and enables fine-grained cost analysis for ROS2 applications' communication with multiple cloud providers. As each provider offers different options for CPU, GPU, memory, and latency, it can be very difficult for users to decide which to choose. FogROS2-Sky includes an optimization algorithm, which either finds the best available hardware specification that fulfills the user's latency and cost constraints or reports that such a specification does not exist. We use FogROS2-Sky to perform time-cost analysis on three robotics applications: visual SLAM, grasp planning, and motion planning. We are able to sample different hardware setups at nearly half the cost while still create cost and latency functions suitable for the optimizer. We also evaluate the optimizer's efficacy for these applications with the Pareto frontier and show that the optimizer selects efficient hardware configurations to balance cost and latency. Videos and code are available on the website https://sites.google.com/view/fogros2-sky

The Teenager's Problem: Efficient Garment Decluttering With Grasp Optimization

This paper addresses the ''Teenager's Problem'': efficiently removing scattered garments from a planar surface. As grasping and transporting individual garments is highly inefficient, we propose analytical policies to select grasp locations for multiple garments using an overhead camera. Two classes of methods are considered: depth-based, which use overhead depth data to find efficient grasps, and segment-based, which use segmentation on the RGB overhead image (without requiring any depth data); grasp efficiency is measured by Objects per Transport, which denotes the average number of objects removed per trip to the laundry basket. Experiments suggest that both depth- and segment-based methods easily reduce Objects per Transport (OpT) by $20\%$; furthermore, these approaches complement each other, with combined hybrid methods yielding improvements of $34\%$. Finally, a method employing consolidation (with segmentation) is considered, which manipulates the garments on the work surface to increase OpT; this yields an improvement of $67\%$ over the baseline, though at a cost of additional physical actions.

FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity

The Robot Operating System (ROS2) is the most widely used software platform for building robotics applications. FogROS2 extends ROS2 to allow robots to access cloud computing on demand. However, ROS2 and FogROS2 assume that all robots are locally connected and that each robot has full access and control of the other robots. With applications like distributed multi-robot systems, remote robot control, and mobile robots, robotics increasingly involves the global Internet and complex trust management. Existing approaches for connecting disjoint ROS2 networks lack key features such as security, compatibility, efficiency, and ease of use. We introduce FogROS2-SGC, an extension of FogROS2 that can effectively connect robot systems across different physical locations, networks, and Data Distribution Services (DDS). With globally unique and location-independent identifiers, FogROS2-SGC securely and efficiently routes data between robotics components around the globe. FogROS2-SGC is agnostic to the ROS2 distribution and configuration, is compatible with non-ROS2 software, and seamlessly extends existing ROS2 applications without any code modification. Experiments suggest FogROS2-SGC is 19x faster than rosbridge (a ROS2 package with comparable features, but lacking security). We also apply FogROS2-SGC to 4 robots and compute nodes that are 3600km apart. Videos and code are available on the project website https://sites.google.com/view/fogros2-sgc.

Learning to Trace and Untangle Semi-planar Knots (TUSK)

This paper extends prior work on untangling long cables and presents TUSK (Tracing to Untangle Semi-planar Knots), a learned cable-tracing algorithm that resolves over-crossings and undercrossings to recognize the structure of knots and grasp points for untangling from a single RGB image. This work focuses on semi-planar knots, which are knots composed of crossings that each include at most 2 cable segments. We conduct experiments on long cables (3 m in length) with up to 15 semi-planar crossings across 6 different knot types. Crops of crossings from 3 knots (overhand, figure 8, and bowline) of the 6 are seen during training, but none of the full knots are seen during training. This is an improvement from prior work on long cables that can only untangle 2 knot types. Experiments find that in settings with multiple identical cables, TUSK can trace a single cable with 81% accuracy on 7 new knot types. In single-cable images, TUSK can trace and identify the correct knot with 77% success on 3 new knot types. We incorporate TUSK into a bimanual robot system and find that it successfully untangles 64% of cable configurations, including those with new knots unseen during training, across 3 levels of difficulty. Supplementary material, including an annotated dataset of 500 RGB-D images of a knotted cable along with ground-truth traces, can be found at https://sites.google.com/view/tusk-rss.

Learning to Efficiently Plan Robust Frictional Multi-Object Grasps

We consider a decluttering problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface and must be efficiently transported to a packing box using both single and multi-object grasps. Prior work considered frictionless multi-object grasping. In this paper, we introduce friction to increase picks per hour. We train a neural network using real examples to plan robust multi-object grasps. In physical experiments, we find an 11.7% increase in success rates, a 1.7x increase in picks per hour, and an 8.2x decrease in grasp planning time compared to prior work on multi-object grasping. Videos are available at https://youtu.be/pEZpHX5FZIs.

SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception

Cables are commonplace in homes, hospitals, and industrial warehouses and are prone to tangling. This paper extends prior work on autonomously untangling long cables by introducing novel uncertainty quantification metrics and actions that interact with the cable to reduce perception uncertainty. We present Sliding and Grasping for Tangle Manipulation 2.0 (SGTM 2.0), a system that autonomously untangles cables approximately 3 meters in length with a bilateral robot using estimates of uncertainty at each step to inform actions. By interactively reducing uncertainty, Sliding and Grasping for Tangle Manipulation 2.0 (SGTM 2.0) reduces the number of state-resetting moves it must take, significantly speeding up run-time. Experiments suggest that SGTM 2.0 can achieve 83% untangling success on cables with 1 or 2 overhand and figure-8 knots, and 70% termination detection success across these configurations, outperforming SGTM 1.0 by 43% in untangling accuracy and 200% in full rollout speed. Supplementary material, visualizations, and videos can be found at sites.google.com/view/sgtm2.

Learning Self-Supervised Representations from Vision and Touch for Active Sliding Perception of Deformable Surfaces

Humans make extensive use of vision and touch as complementary senses, with vision providing global information about the scene and touch measuring local information during manipulation without suffering from occlusions. In this work, we propose a novel framework for learning multi-task visuo-tactile representations in a self-supervised manner. We design a mechanism which enables a robot to autonomously collect spatially aligned visual and tactile data, a key property for downstream tasks. We then train visual and tactile encoders to embed these paired sensory inputs into a shared latent space using cross-modal contrastive loss. The learned representations are evaluated without fine-tuning on 5 perception and control tasks involving deformable surfaces: tactile classification, contact localization, anomaly detection (e.g., surgical phantom tumor palpation), tactile search from a visual query (e.g., garment feature localization under occlusion), and tactile servoing along cloth edges and cables. The learned representations achieve an 80% success rate on towel feature classification, a 73% average success rate on anomaly detection in surgical materials, a 100% average success rate on vision-guided tactile search, and 87.8% average servo distance along cables and garment seams. These results suggest the flexibility of the learned representations and pose a step toward task-agnostic visuo-tactile representation learning for robot control.

Autonomously Untangling Long Cables

Cables are ubiquitous in many settings, but are prone to self-occlusions and knots, making them difficult to perceive and manipulate. The challenge often increases with cable length: long cables require more complex slack management and strategies to facilitate observability and reachability. In this paper, we focus on autonomously untangling cables up to 3 meters in length using a bilateral robot. We develop new motion primitives to efficiently untangle long cables and novel gripper jaws specialized for this task. We present Sliding and Grasping for Tangle Manipulation (SGTM), an algorithm that composes these primitives with RGBD vision to iteratively untangle. SGTM untangles cables with success rates of 67% on isolated overhand and figure eight knots and 50% on more complex configurations. Supplementary material, visualizations, and videos can be found at https://sites.google.com/view/rss-2022-untangling/home.