IL

Iolanda Leite

8 records found

SEQUEL

Semi-Supervised Preference-based RL with Query Synthesis via Latent Interpolation

Preference-based reinforcement learning (RL) poses as a recent research direction in robot learning, by allowing humans to teach robots through preferences on pairs of desired behaviours. Nonetheless, to obtain realistic robot policies, an arbitrarily large number of queries is r ...
Rearranging objects is an essential skill for robots. To quickly teach robots new rearrangements tasks, we would like to generate training scenarios from high-level specifications that define the relative placement of objects for the task at hand. Ideally, to guide the robot's le ...
Safety is crucial for autonomous drones to operate close to humans. Besides avoiding unwanted or harmful contact, people should also perceive the drone as safe. Existing safe motion planning approaches for autonomous robots, such as drones, have primarily focused on ensuring phys ...
Practical implementations of deep reinforcement learning (deep RL) have been challenging due to an amplitude of factors, such as designing reward functions that cover every possible interaction. To address the heavy burden of robot reward engineering, we aim to leverage subjectiv ...
Despite the successes of deep reinforcement learning (RL), it is still challenging to obtain safe policies. Formal verification approaches ensure safety at all times, but usually overly restrict the agent's behaviors, since they assume adversarial behavior of the environment. Ins ...

Correct Me If I'm Wrong

Using Non-Experts to Repair Reinforcement Learning Policies

Reinforcement learning has shown great potential for learning sequential decision-making tasks. Yet, it is difficult to anticipate all possible real-world scenarios during training, causing robots to inevitably fail in the long run. Many of these failures are due to variations in ...
Driving styles play a major role in the acceptance and use of autonomous vehicles. Yet, existing motion planning techniques can often only incorporate simple driving styles that are modeled by the developers of the planner and not tailored to the passenger. We present a new appro ...
Many researchers have started to explore natural interaction scenarios for children. No matter if these children are normally developing or have special needs, evaluating Child- Robot Interaction (CRI) is a challenge. To find methods that work well and provide reliable data is di ...