A survey of optimization-based task and motion planning: from classical to learning approaches
Task and motion planning (TAMP) integrates high-level task planning and low-level motion
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …
From Variance to Veracity: Unbundling and Mitigating Gradient Variance in Differentiable Bundle Adjustment Layers
Various pose estimation and tracking problems in robotics can be decomposed into a
correspondence estimation problem (often computed using a deep network) followed by a …
correspondence estimation problem (often computed using a deep network) followed by a …
Learning Differentiable Tensegrity Dynamics using Graph Neural Networks
Tensegrity robots are composed of rigid struts and flexible cables. They constitute an
emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide …
emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide …
Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning
The hybrid nature of multi-contact robotic systems, due to making and breaking contact with
the environment, creates significant challenges for high-quality control. Existing model …
the environment, creates significant challenges for high-quality control. Existing model …
Addressing Stiffness-Induced Challenges in Modeling and Identification for Rigid-Body Systems With Friction and Impacts
M Halm - 2023 - search.proquest.com
Imperfect, useful dynamical models have enabled significant progress in planning and
controlling robotic locomotion and manipulation. Traditionally, these models have been …
controlling robotic locomotion and manipulation. Traditionally, these models have been …
Learning Deep Dynamical Systems using Stable Neural ODEs
Learning complex trajectories from demonstrations in robotic tasks has been effectively
addressed through the utilization of Dynamical Systems (DS). State-of-the-art DS learning …
addressed through the utilization of Dynamical Systems (DS). State-of-the-art DS learning …
Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects
This paper addresses the task of modeling Deformable Linear Objects (DLOs), such as
ropes and cables, during dynamic motion over long time horizons. This task presents …
ropes and cables, during dynamic motion over long time horizons. This task presents …
Instance-Agnostic Geometry and Contact Dynamics Learning
This work presents an instance-agnostic learning framework that fuses vision with dynamics
to simultaneously learn shape, pose trajectories and physical properties via the use of …
to simultaneously learn shape, pose trajectories and physical properties via the use of …