14 research outputs found
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion
Deep reinforcement learning (DRL) is one of the most powerful tools for
synthesizing complex robotic behaviors. But training DRL models is incredibly
compute and memory intensive, requiring large training datasets and replay
buffers to achieve performant results. This poses a challenge for the next
generation of field robots that will need to learn on the edge to adapt to
their environment. In this paper, we begin to address this issue through
observation space quantization. We evaluate our approach using four simulated
robot locomotion tasks and two state-of-the-art DRL algorithms, the on-policy
Proximal Policy Optimization (PPO) and off-policy Soft Actor-Critic (SAC) and
find that observation space quantization reduces overall memory costs by as
much as 4.2x without impacting learning performance.Comment: Accepted to ICRA 202
TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
Model-predictive control (MPC) is a powerful tool for controlling highly
dynamic robotic systems subject to complex constraints. However, MPC is
computationally demanding, and is often impractical to implement on small,
resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC
solver with a low memory footprint targeting the microcontrollers common on
small robots. Our approach is based on the alternating direction method of
multipliers (ADMM) and leverages the structure of the MPC problem for
efficiency. We demonstrate TinyMPC both by benchmarking against the
state-of-the-art solver OSQP, achieving nearly an order of magnitude speed
increase, as well as through hardware experiments on a 27 g quadrotor,
demonstrating high-speed trajectory tracking and dynamic obstacle avoidance.Comment: First three authors contributed equally and are ordered
alphabeticall
Datasheets for Machine Learning Sensors
Machine learning (ML) sensors offer a new paradigm for sensing that enables
intelligence at the edge while empowering end-users with greater control of
their data. As these ML sensors play a crucial role in the development of
intelligent devices, clear documentation of their specifications,
functionalities, and limitations is pivotal. This paper introduces a standard
datasheet template for ML sensors and discusses its essential components
including: the system's hardware, ML model and dataset attributes, end-to-end
performance metrics, and environmental impact. We provide an example datasheet
for our own ML sensor and discuss each section in detail. We highlight how
these datasheets can facilitate better understanding and utilization of sensor
data in ML applications, and we provide objective measures upon which system
performance can be evaluated and compared. Together, ML sensors and their
datasheets provide greater privacy, security, transparency, explainability,
auditability, and user-friendliness for ML-enabled embedded systems. We
conclude by emphasizing the need for standardization of datasheets across the
broader ML community to ensure the responsible and effective use of sensor
data
Project-based, collaborative, algorithmic robotics for high school students: Programming self-driving race cars at MIT
We describe the pedagogy behind the MIT Beaver Works Summer Institute Robotics Program, a new high-school STEM program in robotics. The program utilizes state-of-the-art sensors and embedded computers for mobile robotics. These components are carried on an exciting 1/10-scale race-car platform. The program has three salient, distinguishing features: (i) it focuses on robotics software systems: the students design and build robotics software towards real-world applications, without being distracted by hardware issues; (ii) it champions project-based learning: the students learn through weekly project assignments and a final course challenge; (iii) the learning is implemented in a collaborative fashion: the students learn the basics of collaboration and technical communication in lectures, and they work in teams to design and implement their software systems. The program was offered as a four-week residential program at MIT in the summer of 2016. In this paper, we provide the details of this new program, its teaching objectives, and its results. We also briefly discuss future directions and opportunities
RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance
We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to
evaluate robotics computing performance across a diverse range of hardware
platforms using ROS 2 as its common baseline. The suite encompasses ROS 2
packages covering the full robotics pipeline and integrates two distinct
benchmarking approaches: black-box testing, which measures performance by
eliminating upper layers and replacing them with a test application, and
grey-box testing, an application-specific measure that observes internal system
states with minimal interference. Our benchmarking framework provides
ready-to-use tools and is easily adaptable for the assessment of custom ROS 2
computational graphs. Drawing from the knowledge of leading robot architects
and system architecture experts, RobotPerf establishes a standardized approach
to robotics benchmarking. As an open-source initiative, RobotPerf remains
committed to evolving with community input to advance the future of
hardware-accelerated robotics
Widening Access to Applied Machine Learning with TinyML
Broadening access to both computational and educational resources is critical
to diffusing machine-learning (ML) innovation. However, today, most ML
resources and experts are siloed in a few countries and organizations. In this
paper, we describe our pedagogical approach to increasing access to applied ML
through a massive open online course (MOOC) on Tiny Machine Learning (TinyML).
We suggest that TinyML, ML on resource-constrained embedded devices, is an
attractive means to widen access because TinyML both leverages low-cost and
globally accessible hardware, and encourages the development of complete,
self-contained applications, from data collection to deployment. To this end, a
collaboration between academia (Harvard University) and industry (Google)
produced a four-part MOOC that provides application-oriented instruction on how
to develop solutions using TinyML. The series is openly available on the edX
MOOC platform, has no prerequisites beyond basic programming, and is designed
for learners from a global variety of backgrounds. It introduces pupils to
real-world applications, ML algorithms, data-set engineering, and the ethical
considerations of these technologies via hands-on programming and deployment of
TinyML applications in both the cloud and their own microcontrollers. To
facilitate continued learning, community building, and collaboration beyond the
courses, we launched a standalone website, a forum, a chat, and an optional
course-project competition. We also released the course materials publicly,
hoping they will inspire the next generation of ML practitioners and educators
and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series:
https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin
Widening Access to Applied Machine Learning With TinyML
Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies
The b Subunits in the Peripheral Stalk of F1F0 ATP Synthase Preferentially Adopt an Offset Relationship
The peripheral stalk of F1F0 ATP synthase is essential for the binding of F1 to FO and for proper transfer of energy between the two sectors of the enzyme. The peripheral stalk of Escherichia coli is composed of a dimer of identical b subunits. In contrast, photosynthetic organisms express two b-like genes that form a heterodimeric peripheral stalk. Previously we generated chimeric peripheral stalks in which a portion of the tether and dimerization domains of the E. coli b subunits were replaced with homologous sequences from the b and b\u27 subunits of Thermosynechococcus elongatus (Claggett, S. B., Grabar, T. B., Dunn, S. D., and Cain, B. D. (2007) J. Bacteriol. 189, 5463-5471). The spatial arrangement of the chimeric b and b\u27 subunits, abbreviated Tb and Tb\u27, has been investigated by Cu2+-mediated disulfide cross-link formation. Disulfide formation was studied both in soluble model polypeptides and between full-length subunits within intact functional F1F0 ATP synthase complexes. In both cases, disulfides were preferentially formed between TbA83C and Tb\u27A90C, indicating the existence of a staggered relationship between helices of the two chimeric subunits. Even under stringent conditions rapid formation of disulfides between these positions occurred. Importantly, formation of this cross-link had no detectable effect on ATP-driven proton pumping, indicating that the staggered conformation is compatible with normal enzymatic activity. Under less stringent reaction conditions, it was also possible to detect b subunits cross-linked through identical positions, suggesting that an in-register, nonstaggered parallel conformation may also exist