3 research outputs found
One-Shot Imitation Learning: A Pose Estimation Perspective
In this paper, we study imitation learning under the challenging setting of:
(1) only a single demonstration, (2) no further data collection, and (3) no
prior task or object knowledge. We show how, with these constraints, imitation
learning can be formulated as a combination of trajectory transfer and unseen
object pose estimation. To explore this idea, we provide an in-depth study on
how state-of-the-art unseen object pose estimators perform for one-shot
imitation learning on ten real-world tasks, and we take a deep dive into the
effects that camera calibration, pose estimation error, and spatial
generalisation have on task success rates. For videos, please visit
https://www.robot-learning.uk/pose-estimation-perspective.Comment: Published at the 7th Conference on Robot Learning (CoRL 2023). For
more details please visit
https://www.robot-learning.uk/pose-estimation-perspectiv
Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization
This paper introduces a modular framework for Mixed-variable and
Combinatorial Bayesian Optimization (MCBO) to address the lack of systematic
benchmarking and standardized evaluation in the field. Current MCBO papers
often introduce non-diverse or non-standard benchmarks to evaluate their
methods, impeding the proper assessment of different MCBO primitives and their
combinations. Additionally, papers introducing a solution for a single MCBO
primitive often omit benchmarking against baselines that utilize the same
methods for the remaining primitives. This omission is primarily due to the
significant implementation overhead involved, resulting in a lack of controlled
assessments and an inability to showcase the merits of a contribution
effectively. To overcome these challenges, our proposed framework enables an
effortless combination of Bayesian Optimization components, and provides a
diverse set of synthetic and real-world benchmarking tasks. Leveraging this
flexibility, we implement 47 novel MCBO algorithms and benchmark them against
seven existing MCBO solvers and five standard black-box optimization algorithms
on ten tasks, conducting over 4000 experiments. Our findings reveal a superior
combination of MCBO primitives outperforming existing approaches and illustrate
the significance of model fit and the use of a trust region. We make our MCBO
library available under the MIT license at
\url{https://github.com/huawei-noah/HEBO/tree/master/MCBO}
Toward real-world automated antibody design with combinatorial Bayesian optimization
Antibodies are multimeric proteins capable of highly specific molecular recognition. The complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often dominates antigen-binding specificity. Hence, it is a priority to design optimal antigen-specific CDRH3 to develop therapeutic antibodies. The combinatorial structure of CDRH3 sequences makes it impossible to query binding-affinity oracles exhaustively. Moreover, antibodies are expected to have high target specificity and developability. Here, we present AntBO, a combinatorial Bayesian optimization framework utilizing a CDRH3 trust region for an in silico design of antibodies with favorable developability scores. The in silico experiments on 159 antigens demonstrate that AntBO is a step toward practically viable in vitro antibody design. In under 200 calls to the oracle, AntBO suggests antibodies outperforming the best binding sequence from 6.9 million experimentally obtained CDRH3s. Additionally, AntBO finds very-high-affinity CDRH3 in only 38 protein designs while requiring no domain knowledge