Benoit Landry

ASL Publications

S. Singh, B. Landry, A. Majumdar, J.-J. E. Slotine, and M. Pavone, “Robust Feedback Motion Planning via Contraction Theory,” Int. Journal of Robotics Research, vol. 42, no. 9, pp. 655–688, 2023.

[BibTeX] [Abstract]

Abstract:

@article{SinghLandryEtAl2019,
  author = {Singh, S. and Landry, B. and Majumdar, A. and Slotine, J-J. E. and Pavone, M.},
  title = {Robust Feedback Motion Planning via Contraction Theory},
  journal = {{Int. Journal of Robotics Research}},
  volume = {42},
  number = {9},
  pages = {655--688},
  year = {2023},
  keywords = {pub},
  owner = {ssingh19},
  timestamp = {2019-09-11},
  url = {https://journals.sagepub.com/doi/pdf/10.1177/02783649231186165}
}

B. Landry, H. Dai, and M. Pavone, “SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions,” in Learning for Dynamics & Control Conference, 2021.
[BibTeX] [Abstract]

Abstract: Value functions are powerful abstractions broadly used across optimal control and robotics algorithms. Several lines of work have attempted to leverage trajectory optimization to learn value function approximations, usually by solving a large number of trajectory optimization problems as a means to generate training data. Even though these methods point to a promising direction, for sufficiently complex tasks, their sampling requirements can become computationally intractable. In this work, we leverage insights from adversarial learning in order to improve the sampling efficiency of a simple value function learning algorithm. We demonstrate how generating adversarial samples for this task presents a unique challenge due to the loss function that does not admit a closed form expression of the samples, but that instead requires the solution to a nonlinear optimization problem. Our key insight is that by leveraging duality theory from optimization, it is still possible to compute adversarial samples for this learning problem with virtually no computational overhead, including without having to keep track of shifting distributions of approximation errors or having to train generative models. We apply our method, named SEAGuL, to a canonical control task (balancing the acrobot) and a more challenging and highly dynamic nonlinear control task (the perching of a small glider). We demonstrate that compared to random sampling, with the same number of samples, training value function approximations using SEAGuL leads to improved generalization errors that also translate to control performance improvement.
```
@inproceedings{LandryDaiEtAl2021,
  author = {Landry, B. and Dai, H. and Pavone, M.},
  title = {SEAGuL: Sample Efficient Adversarially Guided Learning of Value Functions},
  booktitle = {{Learning for Dynamics \& Control Conference}},
  year = {2021},
  month = dec,
  url = {http://proceedings.mlr.press/v144/landry21a/landry21a.pdf},
  owner = {blandry},
  timestamp = {2021-03-15}
}
```
S. Roelofs, B. Landry, M. K. Jalil, A. Martin, S. Koppaka, S. K. Y. Tang, and M. Pavone, “Vision-based Autonomous Disinfection of High Touch Surfaces in Indoor Environments,” in Int. Conf. on Control, Automation and Systems, 2021.
[BibTeX] [Abstract]

Abstract: Autonomous systems have played an important role in response to the Covid-19 pandemic. Notably, there have been multiple attempts to leverage Unmanned Aerial Vehicles (UAVs) to disinfect surfaces. Although recent research suggests that surface transmission has a minimal impact in the spread of Covid-19, surfaces do play a significant role in the transmission of many other viruses. Employing UAVs for mass spray disinfection offers several potential advantages including high throughput application of disinfectant, large scale deployment, and the minimization of health risks to sanitation workers. Despite these potential benefits and preliminary usage of UAVs for disinfection, there has been little research into their design and effectiveness. In this work we present an autonomous UAV capable of effectively disinfecting surfaces. We identify relevant parameters such as disinfectant concentration, amount, and application distance required of the UAV to sterilize high touch surfaces such as door handles. Finally, we develop a robotic system that enables the fully autonomous disinfection of door handles in an unstructured, previously unknown environment. To our knowledge, this is the smallest untethered UAV ever built with both full autonomy and spraying capabilities, allowing it to operate in confined indoor settings, and the first autonomous UAV to specifically target high touch surfaces on an individual basis with spray disinfectant, resulting in more efficient use of disinfectant.
```
@inproceedings{RoelofsLandryEtAl,
  author = {Roelofs, S. and Landry, B. and Jalil, M. K. and Martin, A. and Koppaka, S. and Tang, S. K. Y. and Pavone, M.},
  title = {Vision-based Autonomous Disinfection of High Touch Surfaces in Indoor Environments},
  booktitle = {{Int. Conf. on Control, Automation and Systems}},
  year = {2021},
  month = aug,
  url = {https://arxiv.org/pdf/2108.11456.pdf},
  owner = {blandry},
  timestamp = {2021-08-21}
}
```
H. Dai, B. Landry, L. Yang, M. Pavone, and R. Tedrake, “Lyapunov-Stable Neural-Network Control,” in Robotics: Science and Systems, Virtual, 2021.
[BibTeX] [Abstract]

Abstract: Deep learning has had a far reaching impact in robotics. Specifically, deep reinforcement learning algorithms have been highly effective in synthesizing neural-network controllers for a wide range of tasks. However, despite this empirical success, these controllers still lack theoretical guarantees on their performance, such as Lyapunov stability (i.e., all trajectories of the closed-loop system are guaranteed to converge to a goal state under the control policy). This is in stark contrast to traditional model based controller design, where principled approaches (like LQR) can synthesize stable controllers with provable guarantees. To address this gap, we propose a generic method to synthesize a Lyapunov-stable neural-network controller, together with a neural-network Lyapunov function to simultaneously certify its stability. Our approach formulates the Lyapunov condition verification as a mixed-integer linear program (MIP). Our MIP verifier either certifies the Lyapunov condition, or generates counter-examples that can help improve the candidate controller and the Lyapunov function. We also present an optimization program to compute an inner approximation of the region-of-attraction for the closed-loop system. We apply our approach to robots including an inverted pendulum, a 2D and a 3D quadrotor, and showcase that our neural-network controller outperforms a baseline LQR controller.
```
@inproceedings{DaiLandryEtAl2021,
  author = {Dai, H. and Landry, B. and Yang, L. and Pavone, M. and Tedrake, R.},
  title = {Lyapunov-Stable Neural-Network Control},
  booktitle = {{Robotics: Science and Systems}},
  year = {2021},
  address = {Virtual},
  month = jul,
  url = {https://arxiv.org/pdf/2109.14152.pdf},
  owner = {blandry},
  timestamp = {2021-11-07}
}
```
B. Landry, “Differentiable and Bilevel Optimization for Control in Robotics,” PhD thesis, Stanford University, Dept. of Aeronautics and Astronautics, Stanford, California, 2021.
[BibTeX] [Abstract]

Abstract: In this dissertation, we investigate an up-and-coming class of mathematical programs, bilevel optimization, and how it can be leveraged to tackle the most pressing algorithmic challenges of control in robotics. In this dissertation, we give an overview of our work on bilevel optimization, where two mathematical programs are nested into one another, and our progress on leveraging this class of problems to move us closer to computationally tractable control of nonlinear systems. Specifically, we demonstrate how it is possible to design novel solution methods that utilize advances in automatic differentiation while retaining the benefits of state of the art constrained nonlinear optimization solvers. We also demonstrate how particularly challenging problems of nonlinear control such as planning through contact, adversarial learning of value functions, and Lyapunov synthesis can all surprisingly be tackled by explicitly addressing them as bilevel optimization problems.
```
@phdthesis{Landry2021,
  author = {Landry, B.},
  title = {Differentiable and Bilevel Optimization for Control in Robotics},
  school = {{Stanford University, Dept. of Aeronautics and Astronautics}},
  year = {2021},
  address = {Stanford, California},
  month = jun,
  url = {https://stacks.stanford.edu/file/druid:bw199zy3697/LandryPhD-augmented.pdf},
  owner = {bylard},
  timestamp = {2021-12-06}
}
```
H. Dai, B. Landry, M. Pavone, and R. Tedrake, “Counter-Example Guided Synthesis of Neural Network Lyapunov Functions for Piecewise Linear Systems,” in Proc. IEEE Conf. on Decision and Control, Jeju Island, Republic of Korea, 2020.
[BibTeX] [Abstract]

Abstract: We introduce an algorithm for synthesizing and verifying piecewise linear Lyapunov functions to prove global exponential stability of piecewise linear dynamical systems. The Lyapunov functions we synthesize are parameterized by feedforward neural networks with leaky ReLU activation units. To train these neural networks, we design a loss function that measures the maximal violation of the Lyapunov conditions in the state space. We show that this maximal violation can be computed by solving a mixed-integer linear program (MILP). Compared to previous learning-based approaches, our learning approach is able to certify with high precision that the learned neural network satisfies the Lyapunov conditions not only for sampled states, but over the entire state space. Moreover, compared to previous optimization-based approaches that require a pre-specified partition of the state space when synthesizing piecewise Lyapunov functions, our method can automatically search for both the partition and the Lyapunov function simultaneously. We demonstrate our algorithm on both continuous and discrete-time systems, including some for which known strategies for partitioning of the Lyapunov function would require introducing higher order Lyapunov functions.
```
@inproceedings{DaiLandryEtAl2020,
  author = {Dai, H. and Landry, B. and Pavone, M. and Tedrake, R.},
  title = {Counter-Example Guided Synthesis of Neural Network Lyapunov Functions for Piecewise Linear Systems},
  booktitle = {{Proc. IEEE Conf. on Decision and Control}},
  year = {2020},
  address = {Jeju Island, Republic of Korea},
  month = dec,
  url = {http://groups.csail.mit.edu/robotics-center/public_papers/Dai20.pdf},
  owner = {blandry},
  timestamp = {2021-03-15}
}
```
B. Landry, J. Lorenzetti, Z. Manchester, and M. Pavone, “Bilevel Optimization for Planning through Contact: A Semidirect Method,” in Int. Symp. on Robotics Research, Hanoi, Vietnam, 2019.
[BibTeX] [Abstract]

Abstract: Many robotics applications, from object manipulation to locomotion, require planning methods that are capable of handling the dynamics of contact. Trajectory optimization has been shown to be a viable approach that can be made to support contact dynamics. However, the current state-of-the art methods remain slow and are often difficult to get to converge. In this work, we leverage recent advances in bilevel optimization to design an algorithm capable of efficiently generating trajectories that involve making and breaking contact. We demonstrate our method’s efficiency by outperforming an alternative state-of-the-art method on a benchmark problem. We moreover demonstrate the method’s ability to design a simple periodic gait for a quadruped with 15 degrees of freedom and four contact points
```
@inproceedings{LandryLorenzettiEtAl2019,
  author = {Landry, B. and Lorenzetti, J. and Manchester, Z. and Pavone, M.},
  title = {Bilevel Optimization for Planning through Contact: A Semidirect Method},
  booktitle = {{Int. Symp. on Robotics Research}},
  year = {2019},
  address = {Hanoi, Vietnam},
  month = oct,
  url = {https://arxiv.org/pdf/1906.04292.pdf},
  owner = {blandry},
  timestamp = {2020-04-13}
}
```
J. Lorenzetti, B. Landry, S. Singh, and M. Pavone, “Reduced Order Model Predictive Control For Setpoint Tracking,” in European Control Conference, Naples, Italy, 2019.
[BibTeX] [Abstract]

Abstract: Despite the success of model predictive control (MPC), its application to high-dimensional systems, such as flexible structures and coupled fluid/rigid-body systems, remains a largely open challenge due to excessive computational complexity. A promising solution approach is to leverage reduced order models for designing the model predictive controller. In this paper we present a reduced order MPC scheme that enables setpoint tracking while robustly guaranteeing constraint satisfaction for linear, discrete, time-invariant systems. Setpoint tracking is enabled by designing the MPC cost function to account for the steady-state error between the full and reduced order models. Robust constraint satisfaction is accomplished by solving (offline) a set of linear programs to provide bounds on the errors due to bounded disturbances, state estimation, and model approximation. The approach is validated on a synthetic system as well as a high-dimensional linear model of a flexible rod, obtained using finite element methods.
```
@inproceedings{LorenzettiLandryEtAl2019,
  author = {Lorenzetti, J. and Landry, B. and Singh, S. and Pavone, M.},
  title = {Reduced Order Model Predictive Control For Setpoint Tracking},
  booktitle = {{European Control Conference}},
  year = {2019},
  address = {Naples, Italy},
  month = jun,
  url = {https://arxiv.org/pdf/1811.06590.pdf},
  owner = {jlorenze},
  timestamp = {2019-04-26}
}
```
B. Landry, Z. Manchester, and M. Pavone, “A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization,” in Robotics: Science and Systems, Freiburg im Breisgau, Germany, 2019.
[BibTeX] [Abstract]

Abstract: Many problems in modern robotics can be addressed by modeling them as bilevel optimization problems. In this work, we leverage augmented Lagrangian methods and recent advances in automatic differentiation to develop a general-purpose nonlinear optimization solver that is well suited to bilevel optimization. We then demonstrate the validity and scalability of our algorithm with two representative robotic problems, namely robust control and parameter estimation for a system involving contact. We stress the general nature of the algorithm and its potential relevance to many other problems in robotics.
```
@inproceedings{LandryManchesterEtAl2019,
  author = {Landry, B. and Manchester, Z. and Pavone, M.},
  title = {A Differentiable Augmented Lagrangian Method for Bilevel Nonlinear Optimization},
  booktitle = {{Robotics: Science and Systems}},
  year = {2019},
  address = {Freiburg im Breisgau, Germany},
  month = jun,
  url = {https://arxiv.org/pdf/1902.03319.pdf},
  owner = {blandry},
  timestamp = {2019-05-18}
}
```
P. Abtahi, B. Landry, J. J. Yang, M. Pavone, S. Follmer, and J. A. Landay, “Beyond The Force: Using Quadcopters to Appropriate Objects and the Environment for Haptics in Virtual Reality,” in ACM CHI Conf. on Human Factors in Computing Systems, Glasgow, UK, 2019.
[BibTeX] [Abstract]

Abstract: Quadcopters have been used as hovering encountered-type haptic devices in virtual reality. We suggest that quadcopters can facilitate rich haptic interactions beyond force feedback by appropriating physical objects and the environment. We present HoverHaptics, an autonomous safe-to-touch quadcopter and its integration with a virtual shopping experience. HoverHaptics highlights three affordances of quadcopters that enable these rich haptic interactions: (1) dynamic positioning of passive haptics, (2) texture mapping, and (3) animating passive props. We identify inherent challenges of hovering encountered-type haptic devices, such as their limited speed, inadequate control accuracy, and safety concerns. We then detail our approach for tackling these challenges, including the use of display techniques, visuo-haptic illusions, and collision avoidance. We conclude by describing a preliminary study (n = 9) to better understand the subjective user experience when interacting with a quadcopter in virtual reality using these techniques.
```
@inproceedings{AbtahiLandryEtAl2019,
  author = {Abtahi, P. and Landry, B. and Yang, J. J. and Pavone, M. and Follmer, S. and Landay, J. A.},
  title = {Beyond The Force: Using Quadcopters to Appropriate Objects and the Environment for Haptics in Virtual Reality},
  booktitle = {{ACM CHI Conf. on Human Factors in Computing Systems}},
  year = {2019},
  address = {Glasgow, UK},
  month = may,
  url = {https://dl.acm.org/doi/10.1145/3290605.3300589},
  owner = {blandry},
  timestamp = {2020-04-13}
}
```
J. Lorenzetti, M. Chen, B. Landry, and M. Pavone, “Reach-Avoid Games Via Mixed-Integer Second-Order Cone Programming,” in Proc. IEEE Conf. on Decision and Control, Miami Beach, Florida, 2018.
[BibTeX] [Abstract]

Abstract: Reach-avoid games are excellent proxies for studying many problems in robotics and related fields, with applications including multi-robot systems, human-robot interactions, and safety-critical systems. Solving reach-avoid games is however difficult due to the conflicting and asymmetric goals of agents, and trade-offs between optimality, computational complexity, and solution generality are commonly required. This paper seeks to find attacker strategies in reach-avoid games that reduce computational complexity while retaining solution quality by using a receding horizon strategy. To solve for the open-loop strategy fast enough to enable an receding horizon approach, the problem is formulated as a mixed-integer second-order cone program. This formulation leverages the use of sums-of-squares optimization to provide guarantees that the strategy is robust to all possible defender policies. The method is demonstrated through numerical and hardware experiments.
```
@inproceedings{LorenzettiChenEtAl2018,
  author = {Lorenzetti, J. and Chen, M. and Landry, B. and Pavone, M.},
  title = {Reach-Avoid Games Via Mixed-Integer Second-Order Cone Programming},
  booktitle = {{Proc. IEEE Conf. on Decision and Control}},
  year = {2018},
  address = {Miami Beach, Florida},
  month = dec,
  url = {/wp-content/papercite-data/pdf/Lorenzetti.Chen.Landry.Pavone.CDC18.pdf},
  owner = {jlorenze},
  timestamp = {2019-09-25}
}
```
B. Landry, M. Chen, S. Hemley, and M. Pavone, “Reach-Avoid Problems via Sum-of-Squares Optimization and Dynamic Programming,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Madrid, Spain, 2018.
[BibTeX] [Abstract]

Abstract: Reach-avoid problems involve driving a system to a set of desirable configurations while keeping it away from undesirable ones. Providing mathematical guarantees for such scenarios is challenging but have numerous potential practical applications. Due to the challenges, analysis of reach-avoid problems involves making trade-offs between generality of system dynamics, generality of problem setups, optimality of solutions, and computational complexity. In this paper, we combine sum-of-squares optimization and dynamic programming to address the reach-avoid problem, and provide a conservative solution that maintains reaching and avoidance guarantees. Our method is applicable to polynomial system dynamics and to general problem setups, and is more computationally scalable than previous related methods. Through a numerical example involving two single integrators, we validate our proposed theory and compare our method to Hamilton-Jacobi reachability. Having validated our theory, we demonstrate the computational scalability of our method by computing the reach-avoid set of a system with two kinematic cars.
```
@inproceedings{LandryChenEtAl2018,
  author = {Landry, B. and Chen, M. and Hemley, S. and Pavone, M.},
  title = {Reach-Avoid Problems via Sum-of-Squares Optimization and Dynamic Programming},
  booktitle = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}},
  year = {2018},
  address = {Madrid, Spain},
  month = oct,
  url = {https://arxiv.org/pdf/1807.11553.pdf},
  owner = {blandry},
  timestamp = {2018-03-03}
}
```
B. Ichter, B. Landry, E. Schmerling, and M. Pavone, “Perception-Aware Motion Planning via Multiobjective Search on GPUs,” in Int. Symp. on Robotics Research, Puerto Varas, Chile, 2017.
[BibTeX] [Abstract]

Abstract: In this paper we approach the robust motion planning problem through the lens of perception-aware planning, whereby we seek a low-cost motion plan subject to a separate constraint on perception localization quality. To solve this problem we introduce the Multiobjective Perception-Aware Planning (MPAP) algorithm which explores the state space via a multiobjective search, considering both cost and a perception heuristic. This perception-heuristic formulation allows us to both capture the history dependence of localization drift and represent complex modern perception methods. The solution trajectory from this heuristic-based search is then certified via Monte Carlo methods to be robust. The additional computational burden of perception-aware planning is offset through massive parallelization on a GPU. Through numerical experiments the algorithm is shown to find robust solutions in about a second. Finally, we demonstrate MPAP on a quadrotor flying perception-aware and perception-agnostic plans using Google Tango for localization, finding the quadrotor safely executes the perception-aware plan every time, while crashing over 20% of the time on the perception-agnostic due to loss of localization.
```
@inproceedings{IchterLandryEtAl2017,
  author = {Ichter, B. and Landry, B. and Schmerling, E. and Pavone, M.},
  title = {Perception-Aware Motion Planning via Multiobjective Search on {GPUs}},
  booktitle = {{Int. Symp. on Robotics Research}},
  year = {2017},
  address = {Puerto Varas, Chile},
  month = dec,
  url = {https://arxiv.org/pdf/1705.02408.pdf},
  owner = {ichter},
  timestamp = {2018-01-16}
}
```

Contacts:

Benoit Landry

Awards:

ASL Publications