23 accepted papers
Reliable robotic manipulation requires control policies that can accurately represent and adapt to uncertainty arising from contact-rich interactions. Modern data-driven methods mitigate uncertainty through large-scale training and computation, and degrade significantly in performance with limited n…
Automating chicken shoulder deboning requires precise 6-DoF cutting through a partially occluded, deformable, multi-material joint, since contact with the bones presents serious health and safety risks. Our work makes both systems-level and algorithmic contributions to train and deploy a reactive fo…
Recent advances have enabled heterogeneous multi-robot teams to learn complex and effective coordination skills. However, existing neural architectures that support heterogeneous teaming tend to force a trade-off between expressivity and efficiency. Shared-parameter designs prioritize sample effici…
Learning robot manipulation from abundant human videos offers a scalable alternative to costly robot-specific data collection. However, domain gaps across visual, morphological, and physical aspects hinder direct imitation. To effectively bridge the domain gap, we propose ImMimic, an embodiment-agno…
Multi-agent reinforcement learning (MARL) has emerged as a promising solution for learning complex and scalable coordination behaviors in multi-robot systems. However, established MARL platforms (e.g., SMAC and MPE) lack robotics relevance and hardware deployment, leaving multi-robot learning resear…
Learning dexterous manipulation skills presents significant challenges due to complex nonlinear dynamics that underlie the interactions between objects and multi-fingered hands. Koopman operators have emerged as a robust method for modeling such nonlinear dynamics within a linear framework. However,…
When human acquire physical skills (e.g., tool use) from experts, we tend to first learn from merely observing the expert. But this is often insufficient. We then engage in practice, where we try to emulate the expert and ensure that our actions produce similar effects on our environment. Inspired b
Scheduling algorithms for real-world heterogeneous multi-robot teams must be able to reason about temporal uncertainty in the world model in order to create plans that are tolerant to the risk of unexpected delays. To this end, we present a novel sampling-based risk-aware approach for solving Hetero…
Task allocation can enable effective coordination of multi-robot teams to accomplish tasks that are intractable for individual robots. However, existing approaches to task allocation often assume that task requirements or reward functions are known and explicitly specified by the user. In this work,…
Complex, multi-task missions require the coordination of heterogeneous robots at multiple inter-connected levels, such as coalition formation, scheduling, and motion planning. This challenge is exacerbated by dynamic changes, such as sensor and actuator failures, communication loss, and unexpected d
Recent advances in multi-agent reinforcement learning (MARL) are enabling impressive coordination in heterogeneous multi-robot teams. However, existing approaches often overlook the challenge of generalizing learned policies to teams of new compositions, sizes, and robots. While such generalization…
Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practical limitations, such as significant computational burden, inscrutable learned behaviors, sensitivity…
Effective coordination of heterogeneous multi-robot teams requires optimizing allocations, schedules, and motion plans in order to satisfy complex multi-dimensional task requirements. This challenge is exacerbated by the fact that real-world applications inevitably introduce uncertainties into robot…
Effective and fluent close-proximity human-robot interaction requires understanding how humans get habituated to robots and how robot motion affects human comfort. While prior work has identified humans' preferences over robot motion characteristics and studied their influence on comfort, we are yet…
Learning dexterous manipulation policies for multi-fingered robots has been a long-standing challenge in robotics. Existing methods either limit themselves to highly constrained problems and smaller models to achieve extreme sample efficiency or sacrifice sample efficiency to gain capacity to solve…
To realize effective heterogeneous multi-robot teams, researchers must leverage individual robots’ relative strengths and coordinate their individual behaviors. Specifically, heterogeneous multi-robot systems must answer three important questions: who (task allocation), when (scheduling), and how (m…
Multi-robot task allocation (MRTA) problems involve optimizing the allocation of robots to tasks. MRTA problems are known to be challenging when tasks require multiple robots and the team is composed of heterogeneous robots. These challenges are further exacerbated when we need to account for uncert…
We address the problem of adapting robot trajectories to improve safety, comfort, and efficiency in humanrobot collaborative tasks. To this end, we propose CoMOTO, a trajectory optimization framework that utilizes stochastic motion prediction to anticipate the human's motion and adapt the robot's jo…
To realize effective heterogeneous multi-agent teams, we must be able to leverage individual agents' relative strengths. Recent work has addressed this challenge by introducing trait-based task assignment approaches that exploit the agents' relative advantages. These approaches, however, assume that…
We address the problem of learning Hierarchical Task Networks (HTNs) from unannotated task demonstrations, while retaining action execution preferences present in the demonstration data. We show that the problem of learning a complex HTN structure can be made analogous to the problem of series/paral
Complex manipulation tasks often require non-trivial and coordinated movements of different parts of a robot. In this work, we address the challenges associated with learning and reproducing the skills required to execute such complex tasks. Specifically, we decompose a task into multiple subtasks a
We propose a learning framework, named Multi-Coordinate Cost Balancing (MCCB), to address the problem of acquiring point-to-point movement skills from demonstrations. MCCB encodes demonstrations simultaneously in multiple differential coordinates that specify local geometric properties. MCCB generat…
An algorithm for learning the dynamics of point-to-point motions from demonstrations using an autonomous nonlinear dynamical system, named contracting dynamical system primitives (CDSP), is presented. The motion dynamics are approximated using a Gaussian mixture model (GMM) and its parameters are le