Search Results for author:
Found 22 papers, 8 papers with code
Smooth Exploration for Robotic Reinforcement Learning
We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on three different real robots: a tendon-driven elastic robot, a quadruped and an RC car.
Ranked #1 on
Continuous Control
on PyBullet HalfCheetah
Deep Reinforcement Learning with Feedback-based Exploration
We employ binary corrective feedback as a general and intuitive manner to incorporate human intuition and domain knowledge in model-free machine learning.
Random Shadows and Highlights: A new data augmentation method for extreme lighting conditions
In this paper, we propose a new data augmentation method, Random Shadows and Highlights (RSH) to acquire robustness against lighting perturbations.
ILoSA: Interactive Learning of Stiffness and Attractors
Teaching robots how to apply forces according to our preferences is still an open challenge that has to be tackled from multiple engineering perspectives.
Learning to Pick at Non-Zero-Velocity from Interactive Demonstrations
This work investigates how the intricate task of a continuous pick & place (P&P) motion may be learned from humans based on demonstrations and corrections.
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs).
Interactive Imitation Learning in State-Space
Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering.
Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization
In Reinforcement Learning (RL), agents have no incentive to exhibit predictable behaviors, and are often pushed (through e. g. policy entropy regularization) to randomize their actions in favor of exploration.
Using Bayesian Dynamical Systems for Motion Template Libraries
Motor primitives or motion templates have become an important concept for both modeling human motor control as well as generating robot behaviors using imitation learning.
Policy Search for Motor Primitives in Robotics
We compare this algorithm to alternative parametrized policy search methods and show that it outperforms previous methods.
Learning Gaussian Policies from Corrective Human Feedback
We demonstrate that the novel algorithm outperforms the current state-of-the-art in final performance, convergence rate and robustness to erroneous feedback in OpenAI Gym continuous control benchmarks, both for simulated and real human teachers.
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach
However, DRL has several limitations when used in real-world problems (e. g., robotics applications).
DeepKoCo: Efficient latent planning with a task-relevant Koopman representation
This paper presents DeepKoCo, a novel model-based agent that learns a latent Koopman representation from images.
GEM: Glare or Gloom, I Can Still See You -- End-to-End Multimodal Object Detection
We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions.
Learning to Exploit Elastic Actuators for Quadruped Locomotion
Spring-based actuators in legged locomotion provide energy-efficiency and improved performance, but increase the difficulty of controller design.
Learning from Demonstrations of Critical Driving Behaviours Using Driver's Risk Field
To continuously improve the learnt policy, we retrain the IL model with augmented data.
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions.
PARTNR: Pick and place Ambiguity Resolving by Trustworthy iNteractive leaRning
We extend previous works and present the PARTNR algorithm that can detect ambiguities in the trained policy by analyzing multiple modalities in the pick and place poses using topological analysis.
Robotic Fabric Flattening with Wrinkle Direction Detection
Deformable Object Manipulation (DOM) is an important field of research as it contributes to practical tasks such as automatic cloth handling, cable routing, surgical operation, etc.
Robotic Packaging Optimization with Reinforcement Learning
Intelligent manufacturing is becoming increasingly important due to the growing demand for maximizing productivity and flexibility while minimizing waste and lead times.
Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study
The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style.
Robust Multi-Modal Density Estimation
Development of multi-modal, probabilistic prediction models has lead to a need for comprehensive evaluation metrics.
