68 accepted papers
Robots often face manipulation tasks in environments where vision is inadequate due to clutter, occlusions, or poor lighting—for example, reaching a shutoff valve at the back of a sink cabinet or locating a light switch above a crowded shelf. In such settings, robots, much like humans, must rely on …
Imagine the future construction site, hospital, or office with dozens of robots bought from different manufacturers. How can we enable these different systems to effectively move in a shared environment, given that each robot may have its own independent motion planning system? This work show how we…
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for a team of agents. Although several MAPF methods that solve full-horizon MAPF have completeness guarantees, very few MAPF methods that plan partial paths have completeness guarantees. Recent work introduced th
Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle novel tasks by flexibly composing generic skills. Solutions to this problem lie in an infinitely vast space of paramete…
Actor-critic models are a class of model-free deep reinforcement learning (RL) algorithms that have demonstrated effectiveness across various robot learning tasks. While considerable research has focused on improving training stability and data sampling efficiency, most deployment strategies have re…
Intercepting fast moving objects, by its very nature, is challenging because of its tight time constraints. This problem becomes further complicated in the presence of sensor noise because noisy sensors provide, at best, incomplete information, which results in a distribution over target states to b…
Best-first search algorithms such as A* and Weighted A* are widely used tools. However, their high memory requirements often make them impractical for memory-constrained applications, such as on-board planning for interplanetary rovers, drones, and embedded systems. One popular strategy among memory
Diffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mostly been confined to single-robot and small-scale environments due to the high sample complexity of learning multi-robo…
Collaborative robots have the ability to adapt and improve their behavior by learning from their human users. By interactively learning on the job, these robots can both acquire new motor skills and customize their behavior to personal user preferences. However, for this paradigm to be viable, there…
Multi-object rearrangement is a challenging task that requires robots to reason about a physical 3D scene and the effects of a sequence of actions. While traditional task planning methods are shown to be effective for long-horizon manipulation, they require discretizing the continuous state and acti…
Model-based planners and controllers are commonly used to solve complex manipulation problems as they can efficiently optimize diverse objectives and generalize to long horizon tasks. However, they often fail during deployment due to noisy actuation, partial observability and imperfect models. To en
Traditional multi-agent path finding (MAPF) methods try to compute entire collision free start-goal paths, with several algorithms offering completeness guarantees. However, computing partial paths offers significant advantages including faster planning, adaptability to changes, and enabling decentr…
Multi-Agent Path Finding (MAPF) is the problem of effectively finding efficient collision-free paths for a group of agents in a shared workspace. The MAPF community has largely focused on developing high-performance heuristic search methods. Recently, several works have applied various machine learn
Robotic manipulators are essential for future autonomous systems, yet limited trust in their autonomy has confined them to rigid, task-specific systems. The intricate configuration space of manipulators, coupled with the challenges of obstacle avoidance and constraint satisfaction, often makes motio…
Smooth, collision-free motion planning is a fundamental challenge in robotics with a wide range of applications such as automated manufacturing, search \& rescue, underwater exploration, etc. Graphs of Convex Sets (GCS) is a recent method for synthesizing smooth trajectories by decomposing the plann…
We are interested in enabling robots to solve difficult pick-and-place manipulation tasks in cluttered and constrained environments. If the robot does not have collision-free access to the object-of-interest (OoI) which it intends to grasp and extract from the workspace, it must reason about which m…
Trajectory optimization is a widely used technique in robot motion planning for letting the dynamics of the system shape and synthesize complex behaviors. Several previous works have shown its benefits in high-dimensional continuous state spaces and under differential constraints. However, long time
We are interested in studying sports with robots and starting with the problem of intercepting a projectile moving toward a robot manipulator equipped with a shield. To successfully perform this task, the robot needs to (i) detect the incoming projectile, (ii) predict the projectile’s future motion,…
Conflict-Based Search (CBS) is a popular multi-agent path finding (MAPF) solver that employs a low-level single agent planner and a high-level constraint tree to resolve conflicts. The vast majority of modern MAPF solvers focus on improving CBS by reducing the size of this tree through various strat…
Operating under real world conditions is challenging due to the possibility of a wide range of failures induced by execution errors and state uncertainty. In relatively benign settings, such failures can be overcome by retrying or executing one of a small number of hand-engineered recovery strategie…
Typical robotic systems rely on models for planning. Therefore, the quality of the robot's behavior is heavily dependent on how accurately the model can predict the outcome of the robot's actions in the environment. A challenge, however, is that no model is perfect; moreover, we often do not know wh…
Real-world manipulation problems in heavy clutter require robots to reason about potential contacts with objects in the environment. We focus on pick-and-place style tasks to retrieve a target object from a shelf where some ‘movable’ objects must be rearranged in order to solve the task. In particul…
In manipulation tasks like plug insertion or assembly that have low tolerance to errors in pose estimation (errors of the order of 2 mm can cause task failure), the utilization of touch/contact modality can aid in accurately localizing the object of interest. Motivated by this, in this work we model
Robots often have to perform manipulation tasks in close proximity to people (Fig 1). As such, it is desirable to use a robot arm that has limited joint torques so as to not injure the nearby person. Unfortunately, these limited torques then limit the payload capability of the arm. By using contact…
Heuristic search-based motion planning algorithms typically discretise the search space in order to solve the shortest path problem. Their performance is closely related to this discretisation. A fine discretisation allows for better approximations of the continuous search space, but makes the searc…
Search-based techniques have shown great success in motion planning problems such as robotic navigation by discretizing the state space and precomputing motion primitives. However in domains with complex dynamic constraints, constructing motion primitives in a discretized state space is non-trivial.…
Lazy search algorithms have been developed to efficiently solve planning problems in domains where the computational effort is dominated by the cost of edge evaluation. The existing algorithms operate by intelligently balancing computational effort between searching the graph and evaluating edges. H
Incrementalgraph search algorithms such as D* Lite reuse previous, and perhaps partial, searches to expedite subsequent path planning tasks. In this article, we are interested in developing incremental graph search algorithms for path finding problems to simultaneously optimize multiple objectives s
Path planning among dynamic obstacles is a fundamental problem in Robotics with numerous applications. In this work, we investigate a problem called Multi-Objective Path Planning with Dynamic Obstacles (MOPPwDO), which requires finding collision-free Pareto-optimal paths amid obstacles moving along
We consider the problem of completing a set of nn tasks with a human-robot team using minimum effort. In many domains, teaching a robot to be fully autonomous can be counterproductive if there are finitely many tasks to be done. Rather, the optimal strategy is to weigh the cost of teaching a robot a…
It is widely anticipated that Level-44 (L4) autonomous vehicles - vehicles capable of autonomously performing all driving functions under certain conditions - will eventually become available to the public. However, these vehicles are likely to be expensive due to the additional requirements on hard…
In many applications, including logistics and manufacturing, robot manipulators operate in semi-structured environments alongside humans or other robots. These environments are largely static, but they may contain some movable obstacles that the robot must avoid. Manipulation tasks in these applicat…
Given access to accurate dynamical models, modern planning approaches are effective in computing feasible and optimal plans for repetitive robotic tasks. However, it is difficult to model the true dynamics of the real world before execution, especially for tasks requiring interactions with objects w…
Path planning in the presence of dynamic obstacles is a fundamental problem in robotics with widespread applications. A typical approach to such problems is that a robot predicts the trajectories of dynamic obstacles, and plans its path while avoiding them. Such a formulation becomes limiting though…
Quadrotors can achieve aggressive flight by tracking complex maneuvers and rapidly changing directions. Planning for aggressive flight with trajectory optimization could be incredibly fast, even in higher dimensions, and can account for dynamics of the quadrotor, however, only provides a locally opt
Robot manipulation in cluttered scenes often requires contact-rich interactions with objects. It can be more economical to interact via non-prehensile actions, for example, push through other objects to get to the desired grasp pose, instead of deliberate prehensile rearrangement of the scene. For e…
We focus on long-sighted planning for a class of problems with multiple independent tasks that are partially observable and evolve over time. An example problem that falls into this class is a robot waiting multiple tables, referred to as tasks, in a restaurant where customers' satisfaction is parti
Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world is often infeasible. We describe an approach for sim-to-real training that…
In this work we tackle the path planning problem for a 21-dimensional snake robot-like manipulator, navigating a cluttered gas turbine for the purposes of inspection. Heuristic search based approaches are effective planning strategies for common manipulation domains. However, their performance on hi…
Path planning for robotic coverage is the task of determining a collision-free robot trajectory that observes all points of interest in an environment. Robots employed for such tasks are often capable of exercising active control over onboard observational sensors during navigation. We address the p…
Agent control among pedestrians is often approached in one of the three following ways: using predefined behaviors for agent navigation, learning navigation behaviors from data, or search-based planning on a graph where each edge is a feasible action chosen from a set of predefined actions. While th…
Fully autonomous vehicles are expected to share the road with less advanced vehicles for a significant period of time. Furthermore, an increasing number of vehicles on the road are equipped with a variety of low-fidelity sensors which provide some perception and localization data, but not at a high…
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle-free volume in spacetime is very small in these scenarios for the vehicle to drive through. However, that does not mean…
Heuristic-based graph search algorithms like A* are frequently used to solve motion planning problems in many domains. For most practical applications, it is infeasible and unnecessary to pre-compute the graph representing the whole search space. Instead, these algorithms generate the graph incremen…
Pose estimation of known objects is fundamental to tasks such as robotic grasping and manipulation. The need for reliable grasping imposes stringent accuracy requirements on pose estimation in cluttered, occluded scenes in dynamic environments. Modern methods employ large sets of training data to le…
Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives. However, robots operating in the real world often face situations not m…
Use of physics-based simulation as a planning model enables a planner to reason and generate plans that involve non-trivial interactions with the world. For example, grasping a milk container out of a cluttered refrigerator may involve moving a robot manipulator in between other objects, pushing awa…
We consider the task of autonomously unloading boxes from trucks using an industrial manipulator robot. There are multiple challenges that arise: (1) real-time motion planning for a complex robotic system carrying two articulated mechanisms, an arm and a scooper, (2) decision-making in terms of what…
Sampling-based approaches are often favored in robotics for high-dimensional motion planning for their fast exploration of the search space. However, at best they offer asymptotic guarantees on solution quality due to their inherent stochasticity. While planning, the majority of effort is often spen…
Search-based planning for relatively low-dimensional motion-planning problems such as for autonomous navigation and autonomous flight has been shown to be very successful. Such framework relies on laying a grid over a state-space and constructing a set of actions (motion primitives) that connect the…
In this work, we present an approach to planning for humanoid mobility. Humanoid mobility is a challenging problem, as the configuration space for a humanoid robot is intractably large, especially if the robot is capable of performing many types of locomotion. For example, a humanoid robot may be ab…
Both Optimal Control and Search-based Planning are used extensively for path planning and have their own set of advantages and disadvantages. In this paper, we propose an algorithm FOCS (Fusion of Optimal Control and Search) that combines these two classes of approaches together. FOCS finds a path e…
Intervention Autonomous Underwater Vehicle or I-AUV has recently started to grab researchers attention in the last 20 years. Only three I-AUVs have demonstrated autonomous manipulation skills: ALIVE, SAUVIM and GIRONA 500. While prior systems rely on variations of the task-priority redundancy contro…
A fundamental robot perception task is that of identifying and estimating the poses of objects with known 3D models in RGB-D data. While feature-based and discriminative approaches have been traditionally used for this task, recent work on deliberative approaches such as PERCH and D2P have shown imp…
Parts assembly, in a broad sense, is to make multiple objects to be in specific relative poses in contact with each other. One of the major reasons that make it difficult is uncertainty. Because parts assembly involves physical contact between objects, it requires higher precision than other manipul…
The benefits of bidirectional planning over the unidirectional version are well established for motion planning in high-dimensional configuration spaces. While bidirectional approaches have been employed with great success in the context of sampling-based planners such as in RRT-Connect, they have n
We introduce a novel paradigm for model-based multi-object recognition and 3 DoF pose estimation from 3D sensor data that integrates exhaustive global reasoning with discriminatively-trained algorithms in a principled fashion. Typ- ical approaches for this task are based on scene-to-model feature ma…
In many robotic domains such as flexible automated manufacturing or personal assistance, a fundamental perception task is that of identifying and localizing objects whose 3D models are known. Canonical approaches to this problem include discriminative methods that find correspondences between featur
Robots are increasingly being used in situations such as search and rescue that require robust navigation capabilities, potentially in areas with little or no GPS or other high-quality localization information. As more robots are used in these scenarios, it becomes viable to collaborate between hete…
In a cluttered scene, an object is often occluded by other objects, and a robot cannot figure out what the object is and perceive its pose exactly. We assume that the robot is equipped with a depth sensor and given a database of 3D object models and their grasping poses, but yet there is uncertainty
Exploration of unknown environments is an important aspect to fielding teams of robots. Without the ability to determine on their own where to go in the environment, the full potential of robotic teams is limited to the abilities of human operators to deploy them for search and rescue, mapping, or o…
Learning from demonstration (LfD) is a common technique applied to many problems in robotics, such as populating grasp databases, training for reinforcement learning of high-level skill sets and bootstrapping motion planners. While such approaches are generally highly valued, they rely on the often…
Many motion planning problems in robotics are high dimensional planning problems. While sampling-based motion planning algorithms handle the high dimensionality very well, the solution qualities are often hard to control due to the inherent randomization. In addition, they suffer severely when the c…
Many robot applications involve lifelong planning in relatively static environments e.g. assembling objects or sorting mail in an office building. In these types of scenarios, the robot performs many tasks over a long period of time. Thus, the time required for computing a motion plan becomes a sign…
In this paper, we solve the path planning problem for a tethered mobile robot, which is connected to a fixed base by a cable of length L. The reachable space of the robot is restricted by the length of the cable and obstacles. The reachable space of the tethered robot can be computed by considering…
Follow-the-leader based approaches have been popular for the control of multi-robot teams for their ability to drive many with few. Typically, in these methods you select a single leader, generate a plan for it, while all other agents follow this leader using their individual controllers. However, t…
Experience Graphs have been shown to accelerate motion planning using parts of previous paths in an A* framework. Experience Graphs work by computing a new heuristic for weighted A* search on top of the domain's original heuristic and the edges in an Experience Graph. The new heuristic biases the se…
Personal robots need to manipulate a variety of articulated mechanisms as part of day-to-day tasks. These tasks are often specific, goal-driven, and permit very little bootstrap time for learning the articulation type. In this work, we address the problem of purposefully manipulating an articulated…