Tactile sensing plays a pivotal role in human perception and manipulation tasks, allowing us to intuitively understand task dynamics and adapt our actions in real time. Transferring such tactile intelligence to robotic systems would help intelligent agents understand task constraints and accurately interpret the dynamics of both the objects they are interacting with and their own operations. While significant progress has been made in imbuing robots with this tactile intelligence, challenges persist in effectively utilizing tactile information due to the diversity of tactile sensor form factors, manipulation tasks, and learning objectives involved. To address this challenge, we present a unified tactile embedding space capable of predicting a variety of task-centric qualities over multiple manipulation tasks. We collect tactile data from human demonstrations across various tasks and leverage this data to construct a shared latent space for task stage classification, object dynamics estimation, and tactile dynamics prediction. Through experiments and ablation studies, we demonstrate the effectiveness of our shared tactile latent space for more accurate and adaptable tactile networks, showing an improvement of up to 84% over the single-task training.

Cross-pose estimation between rigid objects is a fundamental building block for robotic applications. In this paper, we propose a new cross-pose estimation method that predicts correspondences on a set level as opposed to a point level. This contrasts methods that predict cross-pose from per-point correspondences, which can encounter optimization problems for objects with symmetries, since each point may have multiple valid correspondences. Our method, SCAlign, consists of a Set Correspondence Network (SCN) which predicts these sets and their correspondences, and an alignment module to compute their relative cross-pose. Taking point clouds of two objects as input, SCN predicts a set label for each point such that such that points that share a set label form a cross object correspondence. The alignment module then computes the cross-pose as the SE(3) transformation that aligns these set correspondences. We compare SCAlign against other cross-pose estimation baselines on a synthetically generated dataset, SynWidth, which contains randomly generated width-mate objects with symmetric or near-symmetric intercepts. SCAlign significantly outperforms the baselines on this challenging dataset. Additionally, we show that set correspondences can be leveraged to distinguish positive and negative matches between pegs and holes. Robot experiments further validate the practical application of this approach.

Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments. We propose IFOR, Iterative Flow Minimization for Robotic Object Rearrangement, an end-to-end method for the challenging problem of object rearrangement for unknown objects given an RGBD image of the original and final scenes. First, we learn an optical flow model based on RAFT to estimate the relative transformation of the objects purely from synthetic data. This flow is then used in an iterative minimization algorithm to achieve accurate positioning of previously unseen objects. Crucially, we show that our method applies to cluttered scenes, and in the real world, while training only on synthetic data. Videos are available at h t t ps: //imankgoyal.github.io/ifor.html.

Pose estimation is a basic module in many robot manipulation pipelines. Estimating the pose of objects in the environment can be useful for grasping, motion planning, or manipulation. However, current state-of-the-art methods for pose estimation either rely on large annotated training sets or simulated data. Further, the long training times for these methods prohibit quick interaction with novel objects. To address these issues, we introduce a novel method for zero-shot object pose estimation in clutter. Our approach uses a hypothesis generation and scoring framework, with a focus on learning a scoring function that generalizes to objects not used for training. We achieve zero-shot generalization by rating hypotheses as a function of unordered point differences. We evaluate our method on challenging datasets with both textured and untextured objects in cluttered scenes and demonstrate that our method significantly outperforms previous methods on this task. We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation. Our work allows users to estimate the pose of novel objects without requiring any retraining. Additional information can be found on our website https://bokorn.github.io/zephyr/

When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects. This is of particular interest in the field of autonomous driving, in which many cars, people, bicycles, and other objects need to be accurately tracked. Current state-of-the-art methods require annotated scene flow data from autonomous driving scenes to train scene flow networks with supervised learning. As an alternative, we present a method of training scene flow that uses two self-supervised losses, based on nearest neighbors and cycle consistency. These self-supervised losses allow us to train our method on large unlabeled autonomous driving datasets; the resulting method matches current state-of-the-art supervised performance using no real world annotations and exceeds state-of-the-art performance when combining our self-supervised approach with supervised learning on a smaller labeled dataset.

For robots to operate robustly in the real world, they should be aware of their uncertainty. However, most methods for object pose estimation return a single point estimate of the object's pose. In this work, we propose two learned methods for estimating a distribution over an object's orientation. Our methods take into account both the inaccuracies in the pose estimation as well as the object symmetries. Our first method, which regresses from deep learned features to an isotropic Bingham distribution, gives the best performance for orientation distribution estimation for non-symmetric objects. Our second method learns to compare deep features and generates a non-parameteric histogram distribution. This method gives the best performance on objects with unknown symmetries, accurately modeling both symmetric and non-symmetric objects, without any requirement of symmetry annotation. We show that both of these methods can be used to augment an existing pose estimator. Our evaluation compares our methods to a large number of baseline approaches for uncertainty estimation across a variety of different types of objects. Code available at https://bokorn.github.io/orientation-distributions/.

Cloth detection and manipulation is a common task in domestic and industrial settings, yet such tasks remain a challenge for robots due to cloth deformability. Furthermore, in many cloth-related tasks like laundry folding and bed making, it is crucial to manipulate specific regions like edges and corners, as opposed to folds. In this work, we focus on the problem of segmenting and grasping these key regions. Our approach trains a network to segment the edges and corners of a cloth from a depth image, distinguishing such regions from wrinkles or folds. We also provide a novel algorithm for estimating the grasp location, direction, and directional uncertainty from the segmentation. We demonstrate our method on a real robot system and show that it outperforms baseline methods on grasping success. Video and other supplementary materials are available at: https://sites.google.com/view/cloth-segmentation.