Multi-objective optimization problems are ubiquitous in robotics, eg, the optimization of a
robot manipulation task requires a joint consideration of grasp pose configurations …

In-hand manipulation of objects is an important capability to enable robots to carry-out tasks
which demand high levels of dexterity. This work presents a robot systems approach to …

Three challenges limit the progress of robot learning research: robots are expensive (few
labs can participate), everyone uses different robots (findings do not generalize across labs) …

Although reinforcement learning has seen remarkable progress over the last years, solving
robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In …

In this work, we introduce composable energy policies (CEP), a novel framework for multi-
objective motion generation. We frame the problem of composing multiple policy …

Benchmarks offer a scientific way to compare algorithms using objective performance
metrics. Good benchmarks have two features:(a) they should be widely useful for many …

Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating
progress is difficult due to the cost and diversity of robot hardware. Using Reach, a cloud …

The highly varied and deformable structure of clothing presents a challenging task in the
area of robot manipulation. Recent literature has shown an increasing interest in this field …

We present a comprehensive study on discrete morphological symmetries of dynamical
systems, which are commonly observed in biological and artificial locomoting systems, such …

This paper describes a deep reinforcement learning (DRL) approach that won Phase 1 of
the Real Robot Challenge (RRC) 2021, and then extends this method to a more difficult …