Many optimization problems in robotics involve the optimization of time-expensive black-box functions, such as those involving complex simulations or evaluation of real-world experiments. Furthermore, these functions are often stochastic as repeated experiments are subject to unmeasurable disturbances. Bayesian optimization can be used to optimize such methods in an efficient manner by deploying a probabilistic function estimator to estimate with a given confidence so that regions of the search space can be pruned away. Consequently, the success of the Bayesian optimization depends on the function estimator's ability to provide informative confidence bounds. Existing function estimators require many function evaluations to infer the underlying confidence or depend on modeling of the disturbances. In this paper, it is shown that the confidence bounds provided by the Wilson Score Kernel Density Estimator (WS-KDE) are applicable as excellent bounds to any stochastic function with an output confined to the closed interval [0;1] regardless of the distribution of the output. This finding opens up the use of WS-KDE for stable global optimization on a wider range of cost functions. The properties of WS-KDE in the context of Bayesian optimization are demonstrated in simulation and applied to the problem of automated trap design for vibrational part feeders.

Precise object manipulation and placement is a common problem for household robots, surgery robots, and robots working on in-situ construction. Prior work using computer vision, depth sensors, and reinforcement learning lacks the ability to reactively recover from planning errors, execution errors, or sensor noise. This work introduces a method that uses force-torque sensing to robustly place objects in stable poses, even in adversarial environments. On 46 trials, our method finds success rates of 100% for basic stacking, and 17% for cases requiring adjustment.

In this letter, a paradigm for the classification and manipulation of previously unseen objects is established and demonstrated through a real example of chairs. We present a novel robot manipulation method, guided by the understanding of object stability, perceptibility, and affordance, which allows the robot to prepare previously unseen and randomly oriented chairs for a teddy bear to sit on. Specifically, the robot encounters an unknown object and first reconstructs a complete 3D model from perceptual data via active and autonomous manipulation. By inserting this model into a physical simulator (i.e., the robot's "imagination"), the robot assesses whether the object is a chair and determines how to reorient it properly to be used, i.e., how to reorient it to an upright and accessible pose. If the object is classified as a chair, the robot reorients the object to this pose and seats the teddy bear onto the chair. The teddy bear is a proxy for an elderly person, hospital patient, or child. Experiment results show that our method achieves a high success rate on the real robot task of chair preparation. Also, it outperforms several baseline methods on the task of upright pose prediction for chairs.

Fast, robust, and flexible part feeding is essential for enabling automation of low volume, high variance assembly tasks. An actuated vision-based solution on a traditional vibratory feeder, referred to here as a vision trap, should in principle be able to meet these demands for a wide range of parts. However, in practice, the flexibility of such a trap is limited as an expert is needed to both identify manageable tasks and to configure the vision system. We propose a novel approach to vision trap design in which the identification of manageable tasks is automatic and the configuration of these tasks can be delegated to an automated feeder design system. We show that the trap's capabilities can be formalized in such a way that it integrates seamlessly into the ecosystem of automated feeder design. Our results on six canonical parts show great promise for autonomous configuration of feeder systems.

Robots need to manipulate objects in constrained environments like shelves and cabinets when assisting humans in everyday settings like homes and offices. These constraints make manipulation difficult by reducing grasp accessibility, so robots need to use non-prehensile strategies that leverage object-environment contacts to perform manipulation tasks. To tackle the challenge of planning and controlling contact-rich behaviors in such settings, this work uses Hybrid Force-Velocity Controllers (HFVCs) as the skill representation and plans skill sequences with learned preconditions. While HFVCs naturally enable robust and compliant contact-rich behaviors, solvers that synthesize them have traditionally relied on precise object models and closed-loop feedback on object pose, which are difficult to obtain in constrained environments due to occlusions. We first relax HFVCs' need for precise models and feedback with our HFVC synthesis framework, then learn a point-cloud-based precondition function to classify where HFVC executions will still be successful despite modeling inaccuracies. Finally, we use the learned precondition in a search-based task planner to complete contact-rich manipulation tasks in a shelf domain. Our method achieves a task success rate of $73.2\%$, outperforming the $51.5\%$ achieved by a baseline without the learned precondition. While the precondition function is trained in simulation, it can also transfer to a real-world setup without further fine-tuning. See supplementary materials and videos at https://sites.google.com/view/constrained-manipulation/

Abstract-- Robotic manipulators navigating cluttered shelves or cabinets may find it challenging to avoid contact with obstacles. Indeed, rearranging obstacles may be necessary to access a target. Rather than planning explicit motions that place obstacles into a desired pose, we suggest allowing incidental contacts to rearrange obstacles while monitoring contacts for safety. Bypassing object identification, we present a method for categorizing object motions from tactile data collected from incidental contacts with a capacitive tactile skin on an Allegro Hand. We formalize tactile cues associated with categories of object motion, demonstrating that they can determine with > 90% accuracy whether an object is movable and whether a contact is causing the object to slide stably (safe contact) or tip (unsafe).

In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses. Regrasping is needed whenever a robot's current grasp pose fails to perform desired manipulation tasks. Endowing robots with such an ability has applications in many domains such as manufacturing or domestic services. Yet, it is a challenging task due to the large diversity of geometry in everyday objects and the high dimensionality of the state and action space. In this paper, we propose a system for robots to take partial point clouds of an object and the supporting environment as inputs and output a sequence of pick-and-place operations to transform an initial object grasp pose to the desired object grasp poses. The key technique includes a neural stable placement predictor and a regrasp graph based solution through leveraging and changing the surrounding environment. We introduce a new and challenging synthetic dataset for learning and evaluating the proposed approach. In this dataset, we show that our system is able to achieve 73.3% success rate of regrasping diverse objects.

In this paper, we explore whether a robot can learn to hang arbitrary objects onto a diverse set of supporting items such as racks or hooks. Endowing robots with such an ability has applications in many domains such as domestic services, logistics, or manufacturing. Yet, it is a challenging manipulation task due to the large diversity of geometry and topology of everyday objects. In this paper, we propose a system that takes partial point clouds of an object and a supporting item as input and learns to decide where and how to hang the object stably. Our system learns to estimate the contact point correspondences between the object and supporting item to get an estimated stable pose. We then run a deep reinforcement learning algorithm to refine the predicted stable pose. Then, the robot needs to find a collision-free path to move the object from its initial pose to stable hanging pose. To this end, we train a neural network based collision estimator that takes as input partial point clouds of the object and supporting item. We generate a new and challenging, large-scale, synthetic dataset annotated with stable poses of objects hung on various supporting items and their contact point correspondences. In this dataset, we show that our system is able to achieve a 68.3% success rate of predicting stable object poses and has a 52.1% F1 score in terms of finding feasible paths. Supplemental material and videos are available on our project webpage.

There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. However, these policies can consistently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps. Motivated by such objects, we propose a novel problem setting, Exploratory Grasping, for efficiently discovering reliable grasps on an unknown polyhedral object via sequential grasping, releasing, and toppling. We formalize Exploratory Grasping as a Markov Decision Process, study the theoretical complexity of Exploratory Grasping in the context of reinforcement learning and present an efficient bandit-style algorithm, Bandits for Online Rapid Grasp Exploration Strategy (BORGES), which leverages the structure of the problem to efficiently discover high performing grasps for each object stable pose. BORGES can be used to complement any general-purpose grasping algorithm with any grasp modality (parallel-jaw, suction, multi-fingered, etc) to learn policies for objects in which they exhibit persistent failures. Simulation experiments suggest that BORGES can significantly outperform both general-purpose grasping pipelines and two other online learning algorithms and achieves performance within 5% of the optimal policy within 1000 and 8000 timesteps on average across 46 challenging objects from the Dex-Net adversarial and EGAD! object datasets, respectively. Initial physical experiments suggest that BORGES can improve grasp success rate by 45% over a Dex-Net baseline with just 200 grasp attempts in the real world. See https://tinyurl.com/exp-grasping for supplementary material and videos.

In this article, we present Spar (simultaneous planner for assembly robots), an implemented system that reasons about high-level operational goals, geometric goals, and uncertainty-reduction goals to create task plans for an assembly robot. These plans contain manipulations to achieve the assembly goals and sensory operations to cope with uncertainties in the robot's environment. High-level goals (which we refer to as operational goals) are satisfied by adding operations to the plan using a nonlinear, constraint-posting method. Geometric goals are satisfied by placing constraints on the execution of these operations. If the geometric configuration of the world prevents this, Spar adds new operations to the plan along with the necessary set of constraints on the execution of these operations. When the uncertainty in the world description exceeds that specified by the uncertainty-reduction goals, Spar introduces either sensing operations or manipulations to reduce this uncertainty to acceptable levels. If Spar cannot find a way to sufficiently reduce uncertainties, it augments the plan with sensing operations to be used to verify the execution of the action and, when possible, posts possible error-recovery plans, although at this point, the verification operations and recovery plans are predefined.