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Found 10 papers, 1 papers with code
Dual-Force: Enhanced Offline Diversity Maximization under Imitation Constraints
While many algorithms for diversity maximization under imitation constraints are online in nature, many applications require offline algorithms without environment interactions.
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality
We demonstrate the effectiveness of our method on a local navigation task where a quadruped robot needs to reach the target within a finite horizon.
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms.
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging.
Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
Learning diverse skills is one of the main challenges in robotics.
Physarum-Inspired Multi-Commodity Flow Dynamics
We formulate network design as the problem of constructing a network that efficiently supports a multi-commodity flow problem.
A PTAS for $\ell_p$-Low Rank Approximation
On the algorithmic side, for $p \in (0, 2)$, we give the first $(1+\epsilon)$-approximation algorithm running in time $n^{\text{poly}(k/\epsilon)}$.
Approximation Algorithms for \ell_0-Low Rank Approximation
For small $\psi$, our approximation factor is $1+o(1)$.
Approximation Algorithms for $\ell_0$-Low Rank Approximation
For small $\psi$, our approximation factor is $1+o(1)$.
