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Found 5 papers, 2 papers with code
OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering
State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy.
Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter
Achieving highly accurate dynamic or simulator models that are close to the real robot can facilitate model-based controls (e. g., model predictive control or linear-quadradic regulators), model-based trajectory planning (e. g., trajectory optimization), and decrease the amount of learning time necessary for reinforcement learning methods.
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots
While motion planning of locomotion for legged robots has shown great success, motion planning for legged robots with dexterous multi-finger grasping is not mature yet.
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal while avoiding obstacles in uncertain environments.
Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance
In this paper, we propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties for safer navigation through cluttered environments.
