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state-estimation
2024
Anthony Knittel, Majd Hawasly, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
IEEE International Conference on Robotics and Automation, 2024
Abstract | BibTex | arXiv | Publisher
ICRAautonomous-drivingstate-estimation
Abstract:
Accurate prediction is important for operating an autonomous vehicle in
interactive scenarios. Prediction must be fast, to support multiple
requests from a planner exploring a range of possible futures. The
generated predictions must accurately represent the probabilities of
predicted trajectories, while also capturing different modes of
behaviour (such as turning left vs continuing straight at a junction).
To this end, we present DiPA, an interactive predictor that addresses
these challenging requirements. Previous interactive prediction methods
use an encoding of k-mode-samples, which under-represents the full
distribution. Other methods optimise closest-mode evaluations, which
test whether one of the predictions is similar to the ground-truth, but
allow additional unlikely predictions to occur, over-representing
unlikely predictions. DiPA addresses these limitations by using a
Gaussian-Mixture-Model to encode the full distribution, and optimising
predictions using both probabilistic and closest-mode measures. These
objectives respectively optimise probabilistic accuracy and the ability
to capture distinct behaviours, and there is a challenging trade-off
between them. We are able to solve both together using a novel training
regime. DiPA achieves new state-of-the-art performance on the
INTERACTION and NGSIM datasets, and improves over the baseline (MFP)
when both closest-mode and probabilistic evaluations are used. This
demonstrates effective prediction for supporting a planner on
interactive scenarios.
@article{Knittel2023dipa,
title={{DiPA:} Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving},
author={Anthony Knittel and Majd Hawasly and Stefano V. Albrecht and John Redford and Subramanian Ramamoorthy},
journal={IEEE Robotics and Automation Letters},
volume={8},
number={8},
pages={4887--4894},
year={2023}
}
Dongge Han, Trevor McInroe, Adam Jelley, Stefano V. Albrecht, Peter Bell, Amos Storkey
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots
arXiv:2404.14285, 2024
Abstract | BibTex | arXiv | Code | Website
generalisationstate-estimation
Abstract:
Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation capabilities. However, in applications such as household robotics, a critical gap remains in the personalization of these models to individual user preferences. We introduce LLM-Personalize, a novel framework with an optimization pipeline designed to personalize LLM planners for household robotics. Our LLM-Personalize framework features an LLM planner that performs iterative planning in multi-room, partially-observable household scenarios, making use of a scene graph constructed with local observations. The generated plan consists of a sequence of high-level actions which are subsequently executed by a controller. Central to our approach is the optimization pipeline, which combines imitation learning and iterative self-training to personalize the LLM planner. In particular, the imitation learning phase performs initial LLM alignment from demonstrations, and bootstraps the model to facilitate effective iterative self-training, which further explores and aligns the model to user preferences. We evaluate LLM-Personalize on Housekeep, a challenging simulated real-world 3D benchmark for household rearrangements, and show that LLM-Personalize achieves more than a 30 percent increase in success rate over existing LLM planners, showcasing significantly improved alignment with human preferences.
@misc{han2024llmpersonalize,
title={LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots},
author={Dongge Han and Trevor McInroe and Adam Jelley and Stefano V. Albrecht and Peter Bell and Amos Storkey},
year={2024},
eprint={2404.14285},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
2023
Anthony Knittel, Majd Hawasly, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
IEEE Robotics and Automation Letters, 2023
Abstract | BibTex | arXiv | Publisher
RA-Lautonomous-drivingstate-estimation
Abstract:
Accurate prediction is important for operating an autonomous vehicle in
interactive scenarios. Prediction must be fast, to support multiple
requests from a planner exploring a range of possible futures. The
generated predictions must accurately represent the probabilities of
predicted trajectories, while also capturing different modes of
behaviour (such as turning left vs continuing straight at a junction).
To this end, we present DiPA, an interactive predictor that addresses
these challenging requirements. Previous interactive prediction methods
use an encoding of k-mode-samples, which under-represents the full
distribution. Other methods optimise closest-mode evaluations, which
test whether one of the predictions is similar to the ground-truth, but
allow additional unlikely predictions to occur, over-representing
unlikely predictions. DiPA addresses these limitations by using a
Gaussian-Mixture-Model to encode the full distribution, and optimising
predictions using both probabilistic and closest-mode measures. These
objectives respectively optimise probabilistic accuracy and the ability
to capture distinct behaviours, and there is a challenging trade-off
between them. We are able to solve both together using a novel training
regime. DiPA achieves new state-of-the-art performance on the
INTERACTION and NGSIM datasets, and improves over the baseline (MFP)
when both closest-mode and probabilistic evaluations are used. This
demonstrates effective prediction for supporting a planner on
interactive scenarios.
@article{Knittel2023dipa,
title={{DiPA:} Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving},
author={Anthony Knittel and Majd Hawasly and Stefano V. Albrecht and John Redford and Subramanian Ramamoorthy},
journal={IEEE Robotics and Automation Letters},
volume={8},
number={8},
pages={4887--4894},
year={2023}
}
2022
Morris Antonello, Mihai Dobre, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
Abstract | BibTex | arXiv
IROSautonomous-drivingstate-estimation
Abstract:
Motion prediction of road users in traffic scenes is critical for autonomous driving systems that must take safe and robust decisions in complex dynamic environments. We present a novel motion prediction system for autonomous driving. Our system is based on the Bayesian inverse planning framework, which efficiently orchestrates map-based goal extraction, a classical control-based trajectory generator and an ensemble of light-weight neural networks specialised in motion profile prediction. In contrast to many alternative methods, this modularity helps isolate performance factors and better interpret results, without compromising performance. This system addresses multiple aspects of interest, namely multi-modality, motion profile uncertainty and trajectory physical feasibility. We report on several experiments with the popular highway dataset NGSIM, demonstrating state-of-the-art performance in terms of trajectory error. We also perform a detailed analysis of our system's components, along with experiments that stratify the data based on behaviours, such as change lane versus follow lane, to provide insights into the challenges in this domain. Finally, we present a qualitative analysis to show other benefits of our approach, such as the ability to interpret the outputs.
@inproceedings{antonello2022flash,
title={Flash: Fast and Light Motion Prediction for Autonomous Driving with {Bayesian} Inverse Planning and Learned Motion Profiles},
author={Morris Antonello, Mihai Dobre, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2022}
}
Anthony Knittel, Majd Hawasly, Stefano V. Albrecht, John Redford, Subramanian Ramamoorthy
DiPA: Diverse and Probabilistically Accurate Interactive Prediction
arXiv:2210.06106, 2022
Abstract | BibTex | arXiv
autonomous-drivingstate-estimation
Abstract:
Accurate prediction is important for operating an autonomous vehicle in interactive scenarios. Previous interactive predictors have used closest-mode evaluations, which test if one of a set of predictions covers the ground-truth, but not if additional unlikely predictions are made. The presence of unlikely predictions can interfere with planning, by indicating conflict with the ego plan when it is not likely to occur. Closest-mode evaluations are not sufficient for showing a predictor is useful, an effective predictor also needs to accurately estimate mode probabilities, and to be evaluated using probabilistic measures. These two evaluation approaches, eg. predicted-mode RMS and minADE/FDE, are analogous to precision and recall in binary classification, and there is a challenging trade-off between prediction strategies for each. We present DiPA, a method for producing diverse predictions while also capturing accurate probabilistic estimates. DiPA uses a flexible representation that captures interactions in widely varying road topologies, and uses a novel training regime for a Gaussian Mixture Model that supports diversity of predicted modes, along with accurate spatial distribution and mode probability estimates. DiPA achieves state-of-the-art performance on INTERACTION and NGSIM, and improves over a baseline (MFP) when both closest-mode and probabilistic evaluations are used at the same time.
@misc{brewitt2022verifiable,
title={{DiPA:} Diverse and Probabilistically Accurate Interactive Prediction},
author={Anthony Knittel and Majd Hawasly and Stefano V. Albrecht and John Redford and Subramanian Ramamoorthy},
year={2022},
eprint={2210.06106},
archivePrefix={arXiv},
primaryClass={cs.RO}
}
2017
Stefano V. Albrecht, Subramanian Ramamoorthy
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)
International Joint Conference on Artificial Intelligence, 2017
Abstract | BibTex | arXiv
IJCAIstate-estimationcausal
Abstract:
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF is evaluated in both synthetic processes and a simulated multi-robot warehouse, where it outperformed alternative filtering methods by exploiting passivity.
@inproceedings{ albrecht2017causality,
title = {Exploiting Causality for Selective Belief Filtering in Dynamic {B}ayesian Networks (Extended Abstract)},
author = {Stefano V. Albrecht and Subramanian Ramamoorthy},
booktitle = {Proceedings of the 26th International Joint Conference on Artificial Intelligence},
address = {Melbourne, Australia},
month = {August},
year = {2017}
}
2016
Stefano V. Albrecht, Subramanian Ramamoorthy
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
Journal of Artificial Intelligence Research, 2016
Abstract | BibTex | arXiv | Publisher
JAIRstate-estimationcausal
Abstract:
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and noisy observations. This can be a hard problem in complex processes with large state spaces. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF produces exact belief states under certain assumptions and approximate belief states otherwise, where the approximation error is bounded by the degree of uncertainty in the process. We show empirically, in synthetic processes with varying sizes and degrees of passivity, that PSBF is faster than several alternative methods while achieving competitive accuracy. Furthermore, we demonstrate how passivity occurs naturally in a complex system such as a multi-robot warehouse, and how PSBF can exploit this to accelerate the filtering task.
@article{ albrecht2016causality,
title = {Exploiting Causality for Selective Belief Filtering in Dynamic {B}ayesian Networks},
author = {Stefano V. Albrecht and Subramanian Ramamoorthy},
journal = {Journal of Artificial Intelligence Research},
volume = {55},
pages = {1135--1178},
year = {2016},
publisher = {AI Access Foundation},
note = {DOI: 10.1613/jair.5044}
}