Publications by Teguh Santoso Lembono

Online Multicontact Receding Horizon Planning via Value Function Approximation

Planning multicontact motions in a receding horizon fashion requires a value function to guide the planning with respect to the future, e.g., building momentum to traverse large obstacles. Traditionally, the value function is approximated by computing traj ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Learning to Guide Online Multi-Contact Receding Horizon Planning

Sylvain Calinon, Teguh Santoso Lembono

In Receding Horizon Planning (RHP), it is critical that the motion being executed facilitates the completion of the task, e.g. building momentum to overcome large obstacles. This requires a value function to inform the desirability of robot states. However ...

IEEE2022

Memory of Motion for Initializing Optimization in Robotics

Teguh Santoso Lembono

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

EPFL2022

Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos

Sylvain Calinon, Teguh Santoso Lembono, Rohan Budhiraja

This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balanc ...

IEEE2021

Learning Constrained Distributions of Robot Configurations with Generative Adversarial Network

Sylvain Calinon, Julius Maximilian Jankowski, Emmanuel Pignat, Teguh Santoso Lembono

In high dimensional robotic system, the manifold of the valid configuration space often has a complex shape, especially under constraints such as end-effector orientation or static stability. We propose a generative adversarial network approach to learn th ...

IEEE2021

Optimal Control Combining Emulation and Imitation to Acquire Physical Assistance Skills

Sylvain Calinon, Amirreza Razmjoo Fard, Teguh Santoso Lembono

paper studies exploiting action-level learning (imitation) in the optimal control problem context. Cost functions defined by the optimal control methods are similar to the goal-level learning (emulation) in animals. However, imitating the robot's or others ...

IEEE2021

Optimization of robot configurations for motion planning in industrial riveting

Sylvain Calinon, Hakan Girgin, Teguh Santoso Lembono

Collaborative robots (cobots) have been increasingly used in the industry in recent years. The cobots are often 7-axis kinematically redundant manipulators. Exploiting these redundancies in industrial tasks is still a challenge as the change in the robot j ...

IEEE2021

Probabilistic Iterative LQR for Short Time Horizon MPC

Sylvain Calinon, Teguh Santoso Lembono

Optimal control is often used in robotics for planning a trajectory to achieve some desired behavior, as expressed by the cost function. Most works in optimal control focus on finding a single optimal trajectory, which is then typically tracked by another ...

IEEE2021

Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion

Sylvain Calinon, Teguh Santoso Lembono

In this paper, we propose a framework to build a memory of motion for warm-starting an optimal control solver for the locomotion task of a humanoid robot. We use HPP Loco3D, a versatile locomotion planner, to generate offline a set of dynamically consisten ...

IEEE2020

SCALAR: Simultaneous Calibration of 2-D Laser and Robot Kinematic Parameters Using Planarity and Distance Constraints

Teguh Santoso Lembono

In this paper, we propose SCALAR, a calibration method to simultaneously calibrate the kinematic parameters of a 6-DoF robot and the extrinsic parameters of a 2-D laser range finder (LRF) attached to the robot's flange. The calibration setup requires only ...

2019