Test and Evaluation of Quadrupedal Walking Gaits through Sim2Real Gap
Quantification
- URL: http://arxiv.org/abs/2201.01323v1
- Date: Tue, 4 Jan 2022 19:24:29 GMT
- Title: Test and Evaluation of Quadrupedal Walking Gaits through Sim2Real Gap
Quantification
- Authors: Prithvi Akella, Wyatt Ubellacker, and Aaron D. Ames
- Abstract summary: The authors propose a two-step approach to evaluate and verify a true system's capacity to satisfy its operational objective.
We show that the same procedure can discriminate between different environments by identifying the Sim2Real Gap between a simulator and its hardware counterpart.
- Score: 17.11389201781203
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this letter, the authors propose a two-step approach to evaluate and
verify a true system's capacity to satisfy its operational objective.
Specifically, whenever the system objective has a quantifiable measure of
satisfaction, i.e. a signal temporal logic specification, a barrier function,
etc - the authors develop two separate optimization problems solvable via a
Bayesian Optimization procedure detailed within. This dual approach has the
added benefit of quantifying the Sim2Real Gap between a system simulator and
its hardware counterpart. Our contributions are twofold. First, we show
repeatability with respect to our outlined optimization procedure in solving
these optimization problems. Second, we show that the same procedure can
discriminate between different environments by identifying the Sim2Real Gap
between a simulator and its hardware counterpart operating in different
environments.
Related papers
- Backscatter Device-aided Integrated Sensing and Communication: A Pareto Optimization Framework [59.30060797118097]
Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban non-line-of-sight scenarios.<n>This paper proposes a backscatter approximation (BD)-assisted ISAC system, which leverages passive BDs naturally distributed in environments of enhancement.
arXiv Detail & Related papers (2025-07-12T17:11:06Z) - Multi-objective Bayesian Optimization With Mixed-categorical Design Variables for Expensive-to-evaluate Aeronautical Applications [0.47812237695718757]
This work aims at developing new methodologies to optimize computational costly complex systems.
The proposed surrogate-based method (often called Bayesian optimization) uses adaptive sampling to promote a trade-off between exploration and exploitation.
The effectiveness of proposed method was tested on practical aeronautical applications within the context of the European Project AGILE 4.0.
arXiv Detail & Related papers (2025-04-14T06:44:13Z) - Stochastic Optimization with Optimal Importance Sampling [49.484190237840714]
We propose an iterative-based algorithm that jointly updates the decision and the IS distribution without requiring time-scale separation between the two.
Our method achieves the lowest possible variable variance and guarantees global convergence under convexity of the objective and mild assumptions on the IS distribution family.
arXiv Detail & Related papers (2025-04-04T16:10:18Z) - Review, Refine, Repeat: Understanding Iterative Decoding of AI Agents with Dynamic Evaluation and Selection [71.92083784393418]
Inference-time methods such as Best-of-N (BON) sampling offer a simple yet effective alternative to improve performance.
We propose Iterative Agent Decoding (IAD) which combines iterative refinement with dynamic candidate evaluation and selection guided by a verifier.
arXiv Detail & Related papers (2025-04-02T17:40:47Z) - Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach [40.58350379106314]
falsification problem involves the identification of counterexamples that violate system safety requirements.
We propose a multi-fidelity Bayesian optimization falsification framework that harnesses simulators with varying levels of accuracy.
arXiv Detail & Related papers (2024-09-12T14:51:03Z) - Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems [2.9016548477524156]
We formulate a knowledgeient-based acquisition function to jointly optimize the first and second-stage variables.
We show that differences in the dimension and length scales between the variable types can lead to inefficiencies of the twostep algorithm.
arXiv Detail & Related papers (2024-08-30T16:26:31Z) - Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspective [0.0]
We propose an E2E differentiable decision-focused learning scheme for particle tracking.
We show that differentiable variations of discrete assignment operations allows for efficient network optimization.
We argue that E2E differentiability provides, besides the general availability of gradient information, an important tool for robust particle tracking to mitigate prediction instabilities.
arXiv Detail & Related papers (2024-07-18T11:42:58Z) - Evolutionary Algorithms for Optimizing Emergency Exit Placement in Indoor Environments [0.0]
A cellular-automaton model is used to simulate the behavior of pedestrians in such scenarios.
A metric is proposed to determine how successful or satisfactory an evacuation was.
Two metaheuristic algorithms, namely an iterated greedy and an evolutionary algorithm (EA) are proposed to solve the problem.
arXiv Detail & Related papers (2024-05-28T16:50:42Z) - Task-Oriented Sensing, Computation, and Communication Integration for
Multi-Device Edge AI [108.08079323459822]
This paper studies a new multi-intelligent edge artificial-latency (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC)
We measure the inference accuracy by adopting an approximate but tractable metric, namely discriminant gain.
arXiv Detail & Related papers (2022-07-03T06:57:07Z) - Tree ensemble kernels for Bayesian optimization with known constraints
over mixed-feature spaces [54.58348769621782]
Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search.
Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function.
Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
arXiv Detail & Related papers (2022-07-02T16:59:37Z) - An Actor-Critic Method for Simulation-Based Optimization [6.261751912603047]
We focus on a simulation-based optimization problem of choosing the best design from the feasible space.
We formulate the sampling process as a policy searching problem and give a solution from the perspective of Reinforcement Learning (RL)
Some experiments are designed to validate the effectiveness of proposed algorithms.
arXiv Detail & Related papers (2021-10-31T09:04:23Z) - An automatic differentiation system for the age of differential privacy [65.35244647521989]
Tritium is an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML)
We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML)
arXiv Detail & Related papers (2021-09-22T08:07:42Z) - Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian
Modeling [68.69431580852535]
We introduce a novel GP regression to incorporate the subgroup feedback.
Our modified regression has provably lower variance -- and thus a more accurate posterior -- compared to previous approaches.
We execute our algorithm on two disparate social problems.
arXiv Detail & Related papers (2021-07-07T03:57:22Z) - Inter-class Discrepancy Alignment for Face Recognition [55.578063356210144]
We propose a unified framework calledInter-class DiscrepancyAlignment(IDA)
IDA-DAO is used to align the similarity scores considering the discrepancy between the images and its neighbors.
IDA-SSE can provide convincing inter-class neighbors by introducing virtual candidate images generated with GAN.
arXiv Detail & Related papers (2021-03-02T08:20:08Z) - Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor
Setups [68.8204255655161]
We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras.
The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
arXiv Detail & Related papers (2021-01-12T12:02:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.