1 citations · 2 across the 11 of their papers we have counts for
13 papers
SPARSER: Sparse Variable Projection by Exploiting Separable Structure in Robotic Perception
Nikolas R. Sanderson, Andrew Fishberg, Haoyu Han +5
Robotic perception often requires solving large nonlinear least-squares (NLS) problems. While sparsity has been widely exploited to scale solvers, a complementary and underused str…
: Doubly Non-Negative Relaxations for Deep Neural Networks
Hanna Jiamei Zhang, Alan Papalia, Michael Everett +1
Existing linear program (LP) and semidefinite program (SDP) relaxations for rectified linear unit (ReLU) neural network (NN) verification yield overly-conservative safety guarantee…
Sparse Variable Projection in Robotic Perception: Exploiting Separable Structure for Efficient Nonlinear Optimization
Alan Papalia, Nikolas Sanderson, Haoyu Han +3
Robotic perception often requires solving large nonlinear least-squares (NLS) problems. While sparsity has been well-exploited to scale solvers, a complementary and underexploited…
Practical and Performant Enhancements for Maximization of Algebraic Connectivity
Leonard Jung, Alan Papalia, Kevin Doherty +1
Long-term state estimation over graphs remains challenging as current graph estimation methods scale poorly on large, long-term graphs. To address this, our work advances a current…
Approximating Safety Feedback Without a Safety Oracle via Model Predictive Control
Jeff Pflueger, Michael Everett
Safe decision-making algorithms for control of mobile robots often require the existence of feedback to verify the safety of proposed actions. This feedback is assumed to be direct…
Uncertainty-Aware Ankle Exoskeleton Control
Fatima Mumtaza Tourk, Bishoy Galoaa, Sanat Shajan +3
Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, res…
Verification of Visual Controllers via Compositional Geometric Transformations
Alexander Estornell, Leonard Jung, Michael Everett
Perception-based neural network controllers are increasingly used in autonomous systems that rely on visual inputs to operate in the real world. Ensuring the safety of such systems…
Learning Smooth State-Dependent Traversability from Dense Point Clouds
Zihao Dong, Alan Papalia, Leonard Jung +4
A key open challenge in off-road autonomy is that the traversability of terrain often depends on the vehicle's state. In particular, some obstacles are only traversable from some o…
A Hybrid Framework for Efficient Koopman Operator Learning
Alexander Estornell, Leonard Jung, Alenna Spiro +2
Koopman analysis of a general dynamics system provides a linear Koopman operator and an embedded eigenfunction space, enabling the application of standard techniques from linear an…
Learning Verifiable Control Policies Using Relaxed Verification
Puja Chaudhury, Alexander Estornell, Michael Everett
To provide safety guarantees for learning-based control systems, recent work has developed formal verification methods to apply after training ends. However, if the trained policy…
Contingency Constrained Planning with MPPI within MPPI
Leonard Jung, Alexander Estornell, Michael Everett
For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance…
LiDAR Inertial Odometry And Mapping Using Learned Registration-Relevant Features
Zihao Dong, Jeff Pflueger, Leonard Jung +5
SLAM is an important capability for many autonomous systems, and modern LiDAR-based methods offer promising performance. However, for long duration missions, existing works that ei…
Collision Avoidance Verification of Multiagent Systems with Learned Policies
Zihao Dong, Shayegan Omidshafiei, Michael Everett
For many multiagent control problems, neural networks (NNs) have enabled promising new capabilities. However, many of these systems lack formal guarantees (e.g., collision avoidanc…