16 accepted papers
Motion planning problems frequently exhibit geometric symmetry in both action and dynamics. This symmetric property can be exploited in learning-based solutions such as reinforcement learning during inference time, which allows the learned solution to improve without the need for additional training
Motion planning tasks are often innately invariant to certain geometric transformations, or in other words, symmetric. This property, however, is not always reflected in learned policies that are trained on these tasks. Although this asymmetry can be addressed through data augmentation or additional…
For many tasks, including table tennis, catching, and sword fighting, a critical step is intercepting the incoming object with a robot arm or held tool. Solutions to robot arm interception via learning, specifically reinforcement learning (RL), have become prevalent, as they provide robust solutions…
When a learning solution is needed for different robots, a model is often trained for each robot geometry, even if the robotic task is the same and the robots are structurally similar. In this paper, we address the problem of transfer learning of swept volume predictors for the motion of articulated…
Computation of the volume of space required for a robot to execute a sweeping motion from a start to a goal has long been identified as a critical primitive operation in both task and motion planning. However, swept volume computation is particularly challenging for multi-link robots with geometric…
Coordinated defensive escorts can aid a navigating payload by positioning themselves strategically in order to maintain the safety of the payload from obstacles. In this letter, we present a novel, end-to-end solution for coordinating an escort team for protecting high-value payloads in a space crow
Deep Reinforcement Learning (RL) has recently emerged as a solution for moving obstacle avoidance. Deep RL learns to simultaneously predict obstacle motions and corresponding avoidance actions directly from robot sensors, even for obstacles with different dynamics models. However, deep RL methods ty…
This letter addresses two challenges facing samplingbased kinodynamic motion planning: a way to identify good candidate states for local transitions and the subsequent computationally intractable steering between these candidate states. Through the combination of sampling-based planning, a Rapidly E
Motion planning for autonomous surface vehicles (ASVs) is challenging since surface vessels are nonlinear underactuated kinodynamic systems with often large inertia. Thus, ASV planners must identify long-term trajectories in order to avoid guiding the ASV into inevitable collision states. Furthermor
We present PRM-RL, a hierarchical method for long-range navigation task completion that combines sampling-based path planning with reinforcement learning (RL). The RL agents learn short-range, point-to-point navigation policies that capture robot dynamics and task constraints without knowledge of th…
Identifying collision-free paths over long time windows in environments with stochastically moving obstacles is difficult, in part because long-term predictions of obstacle positions typically have low fidelity, and the region of possible obstacle occupancy is typically large. As a result, planning…
Manual derivation of optimal robot motions for task completion is difficult, especially when a robot is required to balance its actions between opposing preferences. One solution has been proposed to automatically learn near optimal motions with Reinforcement Learning (RL). This has been successful
Motion planning in stochastic dynamic uncertain environments is critical in several applications such as human interacting robots, autonomous vehicles and assistive robots. In order to address these complex applications, several methods have been developed. The most successful methods often predict…
Highly dynamic environments pose a particular challenge for motion planning due to the need for constant evaluation or validation of plans. However, due to the wide range of applications, an algorithm to safely plan in the presence of moving obstacles is required. In this paper, we propose a novel t…
Physical stochastic disturbances, such as wind, often affect the motion of robots that perform complex tasks in real-world conditions. These disturbances pose a control challenge because resulting drift induces uncertainty and changes in the robot's speed and direction. This paper presents an online…
Motion planning in stochastic dynamic environments is difficult due to the need for constant plan adjustment caused by the uncertainty of the environment. There are many motion planning problems, including flight coordination and autonomous vehicles, that require an algorithm to predict obstacle mot…