diff --git a/_projects/capo.md b/_projects/capo.md
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+++ b/_projects/capo.md
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+---
+title: Control and Actuator Placement Optimization for Large-Scale Problems with Nonlinear Dynamics
+
+description: |
+ A scalable optimization formulation for the simultaneous control and actuator selection and placement for large scale robot systems.
+
+people:
+ - mitch
+ - giusy
+ - zac
+
+layout: project
+image: "/img/capo/Presentation.gif"
+last-updated: 2024-12-09
+---
+
+Actuator selection and placement are critical aspects of robot design that are tightly coupled to task performance. Jointly optimizing actuator placement and control policies or trajectories can enhance efficiency and robustness. However, current optimization methods struggle to scale to high-dimensional systems with many degrees of freedom, including soft robots, large flexible spacecraft, or cloth manipulation.
+We propose CAPO, a scalable Control and Actuator Placement Optimization method that jointly optimizes the number, type, and placement of actuators, along with control trajectories. CAPO concurrently optimizes over all the control and design parameters in a single computationally tractable nonlinear program that scales favorably with system size and complexity. CAPO is evaluated against a state-of-the-art genetic algorithm and mixed-integer programming solvers on six problems, including an acrobatic multirotor aircraft, a spinning space structure, cloth manipulation, and a soft robotic swimmer. On small-scale problems, CAPO finds comparable solutions with similar objective values in 2.5-218x less time than existing methods. On large-scale problems, CAPO is the only method capable of finding a feasible solution, and it achieves actuator configurations that reduce the total number of actuators by 12\%-27.5\% compared to baselines.
+
+- [Paper](../papers/CAPO_WAFR_2024.pdf)
diff --git a/_projects/collision_free_corridors.md b/_projects/collision_free_corridors.md
new file mode 100644
index 0000000..cef7e08
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+++ b/_projects/collision_free_corridors.md
@@ -0,0 +1,21 @@
+---
+title: Differentiable Collision-Free Parametric Corridors
+
+description: |
+ A differentiable collision-free corridor generator.
+
+people:
+ - jon_arrizabalaga
+ - zac
+
+layout: project
+image: "/img/corrgen.gif"
+last-updated: 2024-07-24
+---
+
+This paper presents a method to compute differentiable collision-free parametric corridors. In contrast to existing solutions that decompose the obstacle-free space into multiple convex sets, the continuous corridors computed by our method are smooth and differentiable, making them compatible with existing numerical techniques for learning and optimization. To achieve this, we represent the collision-free corridors as a path-parametric off-centered ellipse with a polynomial basis. We show that the problem of maximizing the volume of such corridors is convex, and can be efficiently solved. To assess the effectiveness of the proposed method, we examine its performance in a synthetic case study and subsequently evaluate its applicability in a real-world scenario from the KITTI dataset.
+
+# Resources
+* Code: https://github.com/jonarriza96/corrgen
+* Video: https://youtu.be/MvC7bPodXz8
+* Paper: https://arxiv.org/pdf/2407.12283
diff --git a/_projects/cvx_cola.md b/_projects/cvx_cola.md
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index 0000000..2ab05ee
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+++ b/_projects/cvx_cola.md
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+---
+title: Convex Maneuver Planning for Spacecraft Collision Avoidance
+
+description: |
+ An algorithm to design globally optimal low-thrust collision-avoidance maneuvers for short-term conjunction events.
+
+people:
+ - fausto
+ - jon_arrizabalaga
+ - zac
+
+layout: project
+image: "/img/spacecraft_cola.png"
+last-updated: 2025-12-01
+---
+
+Conjunction analysis and maneuver planning for spacecraft collision avoidance remains a manual and time-consuming process, typically involving repeated forward simulations of hand-designed maneuvers. With the growing density of satellites in low-Earth orbit (LEO), autonomy is becoming essential for efficiently evaluating and mitigating collisions.
+In this work, we present an algorithm to design low-thrust collision-avoidance maneuvers for short-term conjunction events. We first formulate the problem as a nonconvex quadratically-constrained quadratic program (QCQP), which we then relax into a convex semidefinite program (SDP) using Shor's relaxation. We demonstrate empirically that the relaxation is tight, which enables the recovery of globally optimal solutions to the original nonconvex problem. Our formulation produces a minimum-energy solution while ensuring a desired probability of collision at the time of closest approach. Finally, if the desired probability of collision cannot be satisfied, we relax this constraint into a penalty, yielding a minimum-risk solution. We validate our algorithm with a high-fidelity simulation of a satellite conjunction in low-Earth orbit with a simulated conjunction data message (CDM), demonstrating its effectiveness
+in reducing collision risk.
+
+Checkout our code:
+* [Collision Avoidance Maneuver Planning Code](https://github.com/RoboticExplorationLab/cvx-spacecraft-cola)
diff --git a/_projects/cvx_mpc_stationkeeping.md b/_projects/cvx_mpc_stationkeeping.md
new file mode 100644
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+++ b/_projects/cvx_mpc_stationkeeping.md
@@ -0,0 +1,20 @@
+---
+title: Convex Model Predictive Control for Stationkeeping Control of Halo Orbits
+
+description: |
+ A convex model predictive control approach for stationkeeping maneuver design of unstable halo orbits.
+
+people:
+ - fausto
+ - zac
+
+layout: project
+image: "/img/earth_moon_halo.png"
+last-updated: 2025-03-11
+---
+
+We present a series of works using convex model predictive control for stationkeeping maneuver design of unstable halo orbits. One work proives fuel-optimal impulsive stationkeeping maneuvers as well as a safety plan thorughout the entire mission. The other work uses a solar sail propulsion model to generate the maneuvers. We validate our algorithms in simulations of the three-body Earth-Moon and Saturn-Enceladus systems demonstrating the effectiveness of our approach.
+
+Checkout our code:
+* [Contingency-Aware Stationkeeping Code](https://github.com/RoboticExplorationLab/cvx-mpc-stationkeeping)
+* [Solar-Sailing Stationkeeping Code](https://github.com/RoboticExplorationLab/cvx-mpc-solarsailing)
\ No newline at end of file
diff --git a/_projects/herds.md b/_projects/herds.md
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+++ b/_projects/herds.md
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+---
+title: High-Expansion-Ratio Deployable Structures for Long Duration Space Missions
+
+description: |
+ A hierarchical mechanism capable of 50x expansion ratios enabling kilometer scale structures from a single launch.
+
+people:
+ - mitch
+ - zac
+
+layout: project
+image: "/img/herds/output2.gif"
+last-updated: 2024-01-22
+---
+
+Artificial gravity is a crucial technology to enable long-duration human space flight. However, a kilometer-scale rotating space structure is needed to generate artificial gravity at rotation rates that can be tolerated comfortably by crew. Constructing such a structure with current technology would require many launches and significant in-space assembly. This work presents HERDS, High-Expansion-Ratio Deployable Structures, a hierarchical expansion mechanism that can deploy a kilometer-scale structure from a single launch. HERDS leverages a hierarchical combination of a Kresling mechanism and a pop-up extending truss (PET), a novel variant of the scissor mechanism. We show that HERDS designs achieve 4-11x better beam member aspect ratios than non-hierarchical Kresling or scissor mechanisms, resulting in a stiffer deployed structure. Furthermore, HERDS designs are shown in simulation to satisfy the necessary loading and structural constraints for supporting the Lunar Gateway mission with a factor of safety greater than 1.5 using existing launch vehicles. Our modeling and analysis is validated on a 1/10 scale prototype with a 50x expansion ratio.
+
+- [Paper](../papers/HERDs_Fogelson_Thomas_Manchester_Lipton_Falcon_IEEE_Aero_01_12_24.pdf)
diff --git a/_projects/linear_pixels_to_torques.md b/_projects/linear_pixels_to_torques.md
new file mode 100644
index 0000000..04e3699
--- /dev/null
+++ b/_projects/linear_pixels_to_torques.md
@@ -0,0 +1,22 @@
+---
+title: Pixels to Torques with Linear Feedback
+
+description: |
+ Data-driven, linear output-feedback policies can effectively control robotic systems using vision.
+
+people:
+ - jj
+ - sam
+ - zac
+
+layout: project
+image: "/img/linear_pixels_to_torques/linear_pixels_to_torques.gif"
+last-updated: 2024-12-10
+---
+
+We demonstrate the effectiveness of simple observer-based linear feedback policies for "pixels-to-torques" control of robotic systems using only a robot-facing camera. Specifically, we show that the matrices of an image-based Luenberger observer (linear state estimator) for a "student" output-feedback policy can be learned from demonstration data provided by a "teacher" state-feedback policy via simple linear-least-squares regression. The resulting linear output-feedback controller maps directly from high-dimensional raw images to torques while being amenable to the rich set of analytical tools from linear systems theory, allowing us to enforce closed-loop stability constraints in the learning problem. We also investigate a nonlinear extension of the method via the Koopman embedding. Finally, we demonstrate the surprising effectiveness of linear pixels-to-torques policies on a cartpole system, both in simulation and on real hardware. The policy successfully executes both stabilizing and swing-up trajectory-tracking tasks using only camera feedback while subject to model mismatch, process and sensor noise, perturbations, and occlusions.
+
+# Resources
+* [Algorithm Code](https://github.com/RoboticExplorationLab/LinearPixelsToTorques.jl)
+* [Hardware Code](https://github.com/RoboticExplorationLab/LinearPixelsToTorques-Hardware)
+* [Data](https://drive.google.com/drive/folders/13I-ZCaKQ0d5QbgXJr6Tvcb3gps_EY0CX?usp=sharing)
\ No newline at end of file
diff --git a/_projects/linear_walking.md b/_projects/linear_walking.md
new file mode 100644
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+++ b/_projects/linear_walking.md
@@ -0,0 +1,22 @@
+---
+title: The Surprising Effectiveness of Linear Models for Whole-Body Model-Predictive Control
+
+description: |
+ A single whole-body linearization is sufficient for basic locomotion tasks on humanoids and quadrupeds.
+
+people:
+ - arun
+ - juan
+ - sam
+ - ibrahima
+ - willK
+ - zac
+
+layout: project
+image: "/img/linear_walking.gif"
+last-updated: 2025-10-15
+---
+
+When do locomotion controllers require reasoning about nonlinearities? In this work, we show that a whole-body model-predictive controller using a simple linear time-invariant approximation of the whole-body dynamics is able to execute basic locomotion tasks on complex legged robots. The formulation requires no online nonlinear dynamics evaluations or matrix inversions. We demonstrate walking, disturbance rejection, and even navigation to a goal position without a separate footstep planner on a quadrupedal robot. In addition, we demonstrate dynamic walking on a hydraulic humanoid, a robot with significant limb inertia, complex actuator dynamics, and large sim-to-real gap.
+
+Check out our [website](https://linearwalking.github.io/).
\ No newline at end of file
diff --git a/_projects/mujoco_mpc.md b/_projects/mujoco_mpc.md
new file mode 100644
index 0000000..d70a692
--- /dev/null
+++ b/_projects/mujoco_mpc.md
@@ -0,0 +1,22 @@
+---
+title: Real-Time Whole-Body MPC for Legged Robots with MuJoCo
+
+description: |
+ Real-time whole-body model-predictive control of quadruped and humanoid robotics using the MuJoCo physics engine.
+
+people:
+ - john
+ - taylor
+ - zac
+
+layout: project
+image: "/img/quadruped_handstand.gif"
+last-updated: 2025-03-07
+---
+
+We show that simple model-predictive controls algorithms such as iLQR can be surprisingly effective at enabling a variety of whole-body control behaviors on high dimensional legged robtots (i.e. quadrupeds and humanoids) in the real world.
+
+Checkout our [website](https://johnzhang3.github.io/mujoco_ilqr/).
+
+
+
diff --git a/_projects/soft_capture.md b/_projects/soft_capture.md
new file mode 100644
index 0000000..9510c28
--- /dev/null
+++ b/_projects/soft_capture.md
@@ -0,0 +1,21 @@
+---
+title: Convex Trajectory optimization for the Soft-Capture Problem
+
+description: |
+ A safe and real-time trajectory optimization algorithm for agile spacecraft maneuvers.
+
+people:
+ - ibrahima
+ - zac
+
+layout: project
+image: "/img/soft_capture/approach.gif"
+last-updated: 2024-05-05
+---
+
+We present a fast trajectory optimization algorithm for the soft capture of uncooperative tumbling space objects. Our algorithm generates safe, dynamically feasible, and minimum-fuel trajectories for a six-degree-of-freedom servicing spacecraft to achieve soft capture (near-zero relative velocity at contact) between predefined locations on the servicer spacecraft and target body. We solve a convex problem by enforcing a convex relaxation of the field-of-view constraint, followed by a sequential convex program correcting the trajectory for collision avoidance. The optimization problems can be solved with a standard second-order cone programming solver, making the algorithm both fast and practical for implementation in flight software. We demonstrate the performance and robustness of our algorithm in simulation over a range of object tumble rates up to 10 °/s.
+
+- [Paper](https://arxiv.org/abs/2405.00867)
+
+# Resources
+* [Code Repository](https://github.com/RoboticExplorationLab/SoftCapture)
\ No newline at end of file
diff --git a/_projects/tinympc.md b/_projects/tinympc.md
new file mode 100644
index 0000000..e88c28d
--- /dev/null
+++ b/_projects/tinympc.md
@@ -0,0 +1,47 @@
+---
+title: Model-Predictive Control on Resource-Constrained Microcontrollers
+
+description: |
+ A high-speed MPC solver with a low memory footprint that works on microcontrollers common on small robots.
+
+people:
+ - khai
+ - sam
+ - anoushka
+ - elakhya
+ - zac
+
+layout: project
+image: "/img/tinympc/tinympc.gif"
+last-updated: 2024-03-22
+---
+
+### Paper 1. TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
+Model-predictive control (MPC) is a powerful
+tool for controlling highly dynamic robotic systems subject
+to complex constraints. However, MPC is computationally
+demanding, and is often impractical to implement on small,
+resource-constrained robotic platforms. We present TinyMPC,
+a high-speed MPC solver with a low memory footprint targeting
+the microcontrollers common on small robots. Our approach
+is based on the alternating direction method of multipliers
+(ADMM) and leverages the structure of the MPC problem for
+efficiency. We demonstrate TinyMPC’s effectiveness by benchmarking against the state-of-the-art solver OSQP, achieving
+nearly an order of magnitude speed increase, as well as through
+hardware experiments on a 27 gram quadrotor, demonstrating
+high-speed trajectory tracking and dynamic obstacle avoidance.
+
+### Paper 2. Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC
+Conic constraints appear in many important control applications like legged locomotion, robotic manipulation,
+and autonomous rocket landing. However, current solvers for
+conic optimization problems have relatively heavy computational demands in terms of both floating-point operations and
+memory footprint, making them impractical for use on small
+embedded devices. We extend TinyMPC, an open-source, high-speed solver targeting low-power embedded control applications, to handle second-order cone constraints. We also present
+code-generation software to enable deployment of TinyMPC
+on a variety of microcontrollers. We benchmark our generated
+code against state-of-the-art embedded QP and SOCP solvers,
+demonstrating a two-order-of-magnitude speed increase over
+ECOS while consuming less memory. Finally, we demonstrate
+TinyMPC’s efficacy on the Crazyflie, a lightweight, resource-constrained quadrotor with fast dynamics.
+
+**TinyMPC is publicly available at [https://tinympc.org](https://tinympc.org).**
diff --git a/_projects/trajectory_bundle.md b/_projects/trajectory_bundle.md
new file mode 100644
index 0000000..c6eb96c
--- /dev/null
+++ b/_projects/trajectory_bundle.md
@@ -0,0 +1,18 @@
+---
+title: The Trajectory Bundle Method
+
+description: |
+ Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization for robust motion planning.
+
+people:
+ - kevin
+ - john
+ - jon_arrizabalaga
+ - zac
+
+layout: project
+image: "/img/trajectory_bundle_method.png"
+last-updated: 2025-09-30
+---
+
+We present a unified framework for solving trajectory optimization problems in a derivative-free manner through the use of sequential convex programming. Traditionally, nonconvex optimization problems are solved by forming and solving a sequence of convex optimization problems, where the cost and constraint functions are approximated locally through Taylor series expansions. This presents a challenge for functions where differentiation is expensive or unavailable. In this work, we present a derivative-free approach to form these convex approximations by computing samples of the dynamics, cost, and constraint functions and letting the solver interpolate between them. Our framework includes sample-based trajectory optimization techniques like model-predictive path integral (MPPI) control as a special case and generalizes them to enable features like multiple shooting and general equality and inequality constraints that are traditionally associated with derivative-based sequential convex programming methods. The resulting framework is simple, flexible, and capable of solving a wide variety of practical motion planning and control problems.
diff --git a/_projects/wholebody_mppi.md b/_projects/wholebody_mppi.md
new file mode 100644
index 0000000..9eb3678
--- /dev/null
+++ b/_projects/wholebody_mppi.md
@@ -0,0 +1,20 @@
+---
+title: Whole-body Control of Legged Robots with MPPI
+
+description: |
+ The first successful deployment of a whole-body sampling-based MPC system on a real-world quadruped robot.
+
+people:
+ - juan
+ - john
+ - sofia
+ - zac
+
+layout: project
+image: "/img/whole-body-mppi.gif"
+last-updated: 2025-05-22
+---
+
+Our approach enables real-time, contact-rich locomotion and manipulation using a single control policy derived from Model-Predictive Path Integral (MPPI) control. Demonstrations include box pushing, box climbing, and robust walking over rough terrain.
+
+Check out our [website](https://whole-body-mppi.github.io/).
\ No newline at end of file
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--- a/bib/pubs.bib
+++ b/bib/pubs.bib
@@ -1,3 +1,11 @@
+@article{bishop2025surprising,
+ title={The Surprising Effectiveness of Linear Models for Whole-Body Model-Predictive Control},
+ author={Bishop, Arun L and Alvarez-Padilla, Juan and Schoedel, Sam and Sow, Ibrahima Sory and Chandrachud, Juee and Sharma, Sheitej and Kraus, Will and Park, Beomyeong and Griffin, Robert J and Dolan, John M and Manchester, Zachary},
+ url={http://roboticexplorationlab.org/papers/linear_walking.pdf},
+ journal={IEEE Humanoids},
+ year={2025},
+ publisher = {IEEE}
+}
@article{Zhang2023slomo,
title = {Slomo: A general system for legged robot motion imitation from casual videos},
author = {Zhang, John Z and Yang, Shuo and Yang, Gengshan and Bishop, Arun L and Gurumurthy, Swaminathan and Ramanan, Deva and Manchester, Zachary},
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+
+
+
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Latest News
-
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---
### Lab Meetings
-Anyone is welcome to attend our lab meetings, which are currently being held virtually. See the [lab calendar](http://roboticexplorationlab.org/calendar.html) for details.
+Anyone is welcome to attend our lab meetings, which are currently being held in-person (zoom available). See the [lab calendar](http://roboticexplorationlab.org/calendar.html) for details.
### Prospective Graduate Students
If you are interested in working in our lab, please apply to one of the [graduate programs offered by the Robotics Institute](https://www.ri.cmu.edu/ri-education/). Please contact Zac after you have been admitted.
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