13 accepted papers
Diffusion models have recently emerged as powerful tools for robot motion planning by capturing the multi-modal distribution of feasible trajectories. However, their extension to multi-robot settings with flexible, language-conditioned task specifications remains limited. Furthermore, current diffus
Group symmetries provide a powerful inductive bias for reinforcement learning (RL), enabling efficient generalization across symmetric states and actions via group-invariant Markov Decision Processes (MDPs). However, real-world environments almost never realize fully group-invariant MDPs; dynamics,…
This letter presents a differential geometric control approach that leverages <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SE(3)</i> group invariance and equivariance to increase transferability in learning robot manipulation tasks that involve in
In this paper, we introduce a novel approach for designing invariant control laws through potential functions for fully actuated dynamical systems evolving on manifolds by leveraging the power of neural networks. The geometry and non-linearity inherent to manifold-based dynamical systems pose challe…
Diffusion generative modeling has become a promising approach for learning robotic manipulation tasks from stochastic human demonstrations. In this paper we present Diffusion-EDFs a novel SE(3)-equivariant diffusion-based approach for visual robotic manipulation tasks. We show that our proposed meth…
End-to-end learning for visual robotic manipulation is known to suffer from sample inefficiency, requiring large numbers of demonstrations. The spatial roto-translation equivariance, or the SE(3)-equivariance can be exploited to improve the sample efficiency for learning robotic manipulation. In thi…
Deep reinforcement learning has shown its effectiveness in various applications, providing a promising direction for solving tasks with high complexity. However, naively applying classical RL for learning a complex long-horizon task with a single control policy is inefficient. Thus, policy modulariz
Designing a cost function for nonlinear model predictive control (MPC) with a sparse/binary stage cost is challenging. This paper proposes a novel MPC approach with a scheduled quadratic stage cost function that approximates the true stage cost in order to optimally control a nonlinear system with a…
When designing a controller for the autonomous vehicle system, safety and trajectory tracking performance are two major concerns. This letter proposes a novel control design for an autonomous vehicle system with nonaffine control inputs that can track the desired trajectories while considering the s
A letter entitled “Gaussian Process Online Learning With a Sparse Data Stream” suggests an approach for extending the infinite-horizon Gaussian processes (IHGPs, [1]) to deal with a sparse data stream. We point out that there is an error in differentiating the discrete algebraic Riccati equation (DA
Learning the uncertain dynamical environments for online learning and prediction from noisy sensory measurement streams is essential for various tasks in robotics. Recently, Gaussian process (GP) online learning such as an infinite-horizon Gaussian process (IHGP) has shown effectiveness to cope with…
Gaussian processes (GPs) have been exploited for various applications even including online learning. To learn time-varying hyperparameters from an information-limited sparse data stream, we consider the infinite-horizon Gaussian process (IHGP) with a low computational complexity. For example, the I
Patients with Low Back Pain (LBP) are suggested to follow a protective coping strategy. Therefore, rehabilitation of these patients requires estimating their motor control strategies (the control intent). In this letter, we present an approach that infers the control intent by solving an inverse Mod