4,831 research outputs found
RoboHive: A Unified Framework for Robot Learning
We present RoboHive, a comprehensive software platform and ecosystem for
research in the field of Robot Learning and Embodied Artificial Intelligence.
Our platform encompasses a diverse range of pre-existing and novel
environments, including dexterous manipulation with the Shadow Hand, whole-arm
manipulation tasks with Franka and Fetch robots, quadruped locomotion, among
others. Included environments are organized within and cover multiple domains
such as hand manipulation, locomotion, multi-task, multi-agent, muscles, etc.
In comparison to prior works, RoboHive offers a streamlined and unified task
interface taking dependency on only a minimal set of well-maintained packages,
features tasks with high physics fidelity and rich visual diversity, and
supports common hardware drivers for real-world deployment. The unified
interface of RoboHive offers a convenient and accessible abstraction for
algorithmic research in imitation, reinforcement, multi-task, and hierarchical
learning. Furthermore, RoboHive includes expert demonstrations and baseline
results for most environments, providing a standard for benchmarking and
comparisons. Details: https://sites.google.com/view/robohiveComment: Accepted at 37th Conference on Neural Information Processing Systems
(NeurIPS 2023) Track on Datasets and Benchmark
The potential of programmable logic in the middle: cache bleaching
Consolidating hard real-time systems onto modern multi-core Systems-on-Chip (SoC) is an open challenge. The extensive sharing of hardware resources at the memory hierarchy raises important unpredictability concerns. The problem is exacerbated as more computationally demanding workload is expected to be handled with real-time guarantees in next-generation Cyber-Physical Systems (CPS). A large body of works has approached the problem by proposing novel hardware re-designs, and by proposing software-only solutions to mitigate performance interference. Strong from the observation that unpredictability arises from a lack of fine-grained control over the behavior of shared hardware components, we outline a promising new resource management approach. We demonstrate that it is possible to introduce Programmable Logic In-the-Middle (PLIM) between a traditional multi-core processor and main memory. This provides the unique capability of manipulating individual memory transactions. We propose a proof-of-concept system implementation of PLIM modules on a commercial multi-core SoC. The PLIM approach is then leveraged to solve long-standing issues with cache coloring. Thanks to PLIM, colored sparse addresses can be re-compacted in main memory. This is the base principle behind the technique we call Cache Bleaching. We evaluate our design on real applications and propose hypervisor-level adaptations to showcase the potential of the PLIM approach.Accepted manuscrip
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