41 accepted papers
We address the problem of efficiently organizing search over very large trees, which arises in many applications ranging from autonomous driving to aerial vehicles. Here, we are motivated by off-road autonomy, where real-time planning is essential. Classical approaches use graphs of motion primitive
We consider imitation learning with access only to expert demonstrations, whose real-world application is often limited by covariate shift due to compounding errors during execution. We investigate the effectiveness of the Continuity-based Corrective Labels for Imitation Learning (CCIL) framework in…
We focus on robot navigation in crowded environments. To navigate safely and efficiently within crowds, robots need models for crowd motion prediction. Building such models is hard due to the high dimensionality of multiagent domains and the challenge of collecting or simulating interaction-rich cro…
Sampling-based motion planners rely on incre-mental densification to discover progressively shorter paths. After computing feasible path \xi\xi between start x_{s}x_{s} and goal x_{t}x_{t}, the Informed Set (IS) prunes the configuration space \mathcal{X}\mathcal{X} by conservatively eliminating poin…
We focus on the problem of rearranging a set of objects with a team of car-like robot pushers built using off-the-shelf components. Maintaining control of pushed objects while avoiding collisions in a tight space demands highly coordinated motion that is challenging to execute on constrained hardwar…
We focus on robot navigation in crowded environments. The challenge of predicting the motion of a crowd around a robot makes it hard to ensure human safety and comfort. Recent approaches often employ end-to-end techniques for robot control or deep architectures for high-fidelity human motion predict
We focus on the problem of analyzing multiagent interactions in traffic domains. Understanding the space of behavior of real-world traffic may offer significant advantages for algorithmic design, data-driven methodologies, and bench-marking. However, the high dimensionality of the space and the stoc…
Dexterous manipulation is a challenging and important problem in robotics. While data-driven methods are a promising approach, current benchmarks require simulation or extensive engineering support due to the sample inefficiency of popular methods. We present benchmarks for the TriFinger system, an
When navigating to a goal in an uncertain environment, a robot must simultaneously navigate the exploration-exploitation tradeoff: should it aim to gain information and reduce uncertainty, or should it simply brave the unknown? We formalize this as the Bayesian dynamic motion planning problem, and w…
During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previo…
Efficient and reliable generation of global path plans are necessary for safe execution and deployment of autonomous systems. In order to generate planning graphs which adequately resolve the topology of a given environment, many sampling-based motion planners resort to coarse, heuristically-driven…
Informed and robust decision making in the face of uncertainty is critical for robots operating in unstructured environments. We formulate this as Bayesian Reinforcement Learning over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorit…
The papers in this special section focus on benchmarking protocols for robotic manipulation. Benchmarks are crucial for analyzing the effectiveness of an approach against a common basis, providing a quantitative means for interpreting performance. Carefully designed and widely recognized benchmarks
Autonomous robot-assisted feeding requires the ability to acquire a wide variety of food items. However, it is impossible for such a system to be trained on all types of food in existence. Therefore, a key challenge is choosing a manipulation strategy for a previously unseen food item. Previous work…
A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items. It must adapt to changing user food preferences under uncertain visual and physical environments. Different food items in different environmental conditions require different manipulation strategies…
Benchmarks for robot manipulation are crucial to measuring progress in the field, yet there are few benchmarks that demonstrate critical manipulation skills, possess standardized metrics, and can be attempted by a wide array of robot platforms. To address a lack of such benchmarks, we propose Rubik'
Collision checking is a computational bottleneck in motion planning, requiring lazy algorithms that explicitly reason about when to perform this computation. Optimism in the face of collision uncertainty minimizes the number of checks before finding the shortest path. However, this may take a prohib…
Chopsticks constitute a simple yet versatile tool that humans have used for thousands of years to perform a variety of challenging tasks ranging from food manipulation to surgery. Applying such a simple tool in a diverse repertoire of scenarios requires significant adaptability. Towards developing a…
Addressing uncertainty is critical for autonomous systems to robustly adapt to the real world. We formulate the problem of model uncertainty as a continuous Bayes-Adaptive Markov Decision Process (BAMDP), where an agent maintains a posterior distribution over latent model parameters given a history…
We describe a single fingertip-mounted sensing system for robot manipulation that provides proximity (pre-touch), contact detection (touch), and force sensing (post-touch). The sensor system consists of optical time-of-flight range measurement modules covered in a clear elastomer. Because the elasto…
We present an anytime algorithm that generates a collision-free configuration-space path that closely follows a desired path in task space, according to the discrete Fréchet distance. By leveraging tools from computational geometry, we approximate the search space using a cross-product graph. We use
Autonomous assistive feeding systems need to acquire deformable food items of varying physical characteristics to be able to feed users. However, bite acquisition of these deformable food items is challenging without force feedback of appropriate range and sensitivity. We developed custom solutions…
We present a 16.2-million frame (50-hour) multimodal dataset of two-person face-to-face spontaneous conversations. Our dataset features synchronized body and finger motion as well as audio data. To the best of our knowledge, it represents the largest motion capture and audio dataset of natural conve…
Autonomous feeding is challenging because it requires the manipulation of food items with various compliance, sizes, and shapes. To understand how humans manipulate food items during feeding and to explore ways to adapt their strategies to robots, we collected a rich dataset of human trajectories by
A motion-planning problem's setup can drastically affect the quality of solutions returned by the planner. In this work we consider optimizing these setups, with a focus on doing so in a computationally-efficient fashion. Our approach interleaves optimization with motion planning, which allows us to…
In this paper, we introduce a Markov chain Monte Carlo (MCMC)method to solve multi-objective motion-planning problems. We formulate the problem of finding Pareto-optimal trajectories as a problem of sampling trajectories from a Pareto-optimal set. We define an implicit uniform distribution over the…
We consider the problem of computing shortest paths in a dense motion-planning roadmap G. We assume that n, the number of vertices of G, is very large. Thus, using any path-planning algorithm that directly searches G, running in O(VlogV + E) ≈ O(n2) time, becomes unacceptably expensive. We are there…
Although there is an abundance of planar fiducial-marker systems proposed for augmented reality and computer-vision purposes, using them to estimate the pose accurately in robotic applications where collected data are noisy remains a challenge. This is inherently a difficult problem because these fi…
We estimate the state of a noisy robot arm and underactuated hand using an implicit Manifold Particle Filter (MPF) informed by contact sensors. As the robot touches the world, its state space collapses to a contact manifold that we represent implicitly using a signed distance field. This allows us t…
In this work, we present an anytime planner for creating open-loop trajectories that solve rearrangement planning problems under uncertainty using nonprehensile manipulation. We first extend the Monte Carlo Tree Search algorithm to the unobservable domain. We then propose two default policies that a…
We propose a number of “divergence metrics” to quantify the robustness of a trajectory to state uncertainty for under-actuated or under-sensed systems. These metrics are inspired by contraction analysis and we demonstrate their use to guide randomized planners toward more convergent trajectories thr
We propose a method for generating a configuration space path that closely follows a desired task space path despite the presence of obstacles. We formalize closeness via two path metrics based on the discrete Hausdorff and Frechet distances. Armed with these metrics, we can cast our problem as a tr…
We present POMP (Pareto Optimal Motion Planner), an anytime algorithm for geometric path planning on roadmaps. For robots with several degrees of freedom, collision checks are computationally expensive and often dominate planning time. Our goal is to minimize the number of collision checks for obtai…
This paper addresses the problem of rearrangement planning, i.e. to find a feasible trajectory for a robot that must interact with multiple objects in order to achieve a goal. We propose a planner to solve the rearrangement planning problem by considering two different types of actions: robot-centri
Sampling-based optimal planners, such as RRT*, almost-surely converge asymptotically to the optimal solution, but have provably slow convergence rates in high dimensions. This is because their commitment to finding the global optimum compels them to prioritize exploration of the entire problem domai
We formulate and study the comprehensive multi-root (CMR) planning problem, in which feasible paths are desired between multiple regions. We propose two primary contributions which allow us to extend state-of-the-art sampling-based planners. First, we propose the notion of vertex coloring as a compa…
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques. By recognizing that a set of samples describes an implicit random geometric graph (RGG), we are able to combine the efficient ordered nature of graph-based tec…
In this work we present a fast kinodynamic RRT-planner that uses dynamic nonprehensile actions to rearrange cluttered environments. In contrast to many previous works, the presented planner is not restricted to quasi-static interactions and monotonicity. Instead the results of dynamic robot actions…
We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the comm…
We present a randomized kinodynamic planner that solves rearrangement planning problems. We embed a physics model into the planner to allow reasoning about interaction with objects in the environment. By carefully selecting this model, we are able to reduce our state and action space, gaining tracta…
We present an algorithm for generating open-loop trajectories that solve the problem of rearrangement planning under uncertainty. We frame this as a selection problem where the goal is to choose the most robust trajectory from a finite set of candidates. We generate each candidate using a kinodynami…