arXiv subjects

Gabriel Rodriguez

Publications and source records attributed to Gabriel Rodriguez.

4 recordsLinked to original sources

arXiv2026

Affordance-Based Hierarchical Reinforcement Learning for Quadruped Pedipulation

The object manipulation capabilities of quadruped robots is an open research challenge. While previous studies have focused on low-level policy learning, task execution still relies on expert-designed high-level trajectories. Autonomous selection of both an affordable interaction point on the target object and an affordable robot base pose removes the need for pre-designed trajectories. This study proposes a three-level hierarchical reinforcement learning (RL) framework that utilizes pose affordances to guide the navigation policy, while the navigation policy drives the locomotion policy. In addition, the pedipulation policy is guided by interaction-point affordances, enabling object-centric pose alignment of the quadruped robot and effective end-effector manipulation planning. We train the proposed framework in the IsaacSim ecosystem and evaluate it in both simulation and real-world settings. We investigate the effectiveness of pose affordance across multiple scenarios in simulation while various object interaction tasks are validated on real-world setting forming an object-interaction dataset. The results show that the proposed framework can autonomously identify candidate poses based on their affordance and successfully execute object manipulation tasks in the real world without human guidance.

cs.RO↗

arXiv2026

AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust

Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority, expanding the envelope of possible aggressive maneuvers and enabling inverted flight, perching, and sensing. Prior geometric control approaches extend differential flatness through Hopf fibration-based attitude representations to support bidirectional thrust, but struggle with actuator saturation and motor reversal delay during inversions, requiring heuristic thrust posture scheduling and waypoint tuning. We propose a learning-based framework that modulates a constant reference trajectory to perform compact, position-constrained quadrotor inversions while remaining compatible with traditional trajectory generation and tracking across flight regimes. Separate policies are trained via reinforcement learning for nominal-to-inverted and inverted-to-nominal transitions. In JAX-based simulation, the proposed method achieves the lowest position deviation and settling time across all evaluated baselines, reducing position root mean square error (RMSE) by 32% and settling time by 57% relative to the strongest optimization-based baseline. Hardware experiments demonstrate successful inversion across multiple yaw configurations with position RMSE below 0.35m, and compatibility with downstream trajectory generation and control through circular flight in both regimes. Additionally, we provide an open-source implementation of the proposed framework.

cs.RO↗

arXiv2024

Performance Debugging through Microarchitectural Sensitivity and Causality Analysis

Modern Out-of-Order (OoO) CPUs are complex systems with many components interleaved in non-trivial ways. Pinpointing performance bottlenecks and understanding the underlying causes of program performance issues are critical tasks to fully exploit the performance offered by hardware resources. Current performance debugging approaches rely either on measuring resource utilization, in order to estimate which parts of a CPU induce performance limitations, or on code-based analysis deriving bottleneck information from capacity/throughput models. These approaches are limited by instrumental and methodological precision, present portability constraints across different microarchitectures, and often offer factual information about resource constraints, but not causal hints about how to solve them. This paper presents a novel performance debugging and analysis tool that implements a resource-centric CPU model driven by dynamic binary instrumentation that is capable of detecting complex bottlenecks caused by an interplay of hardware and software factors. Bottlenecks are detected through sensitivity-based analysis, a sort of model parameterization that uses differential analysis to reveal constrained resources. It also implements a new technique we developed that we call causality analysis, that propagates constraints to pinpoint how each instruction contribute to the overall execution time. To evaluate our analysis tool, we considered the set of high-performance computing kernels obtained by applying a wide range of transformations from the Polybench benchmark suite and measured the precision on a few Intel CPU and Arm micro-architectures. We also took one of the benchmarks (correlation) as an illustrative example to illustrate how our tool's bottleneck analysis can be used to optimize a code.

cs.DC↗

arXiv2021

Enhancing condensation on soft substrates through bulk lubricant infusion

Soft substrates such as polydimethylsiloxane (PDMS) enhance droplet nucleation during the condensation of water vapour, because their deformability inherently reduces the energetic threshold for heterogeneous nucleation relative to rigid substrates. However, this enhanced droplet nucleation is counteracted later in the condensation cycle, when the viscoelastic dissipation inhibits condensate droplet shedding from the substrate. Here, we show that bulk lubricant infusion in the soft substrate is a potential pathway for overcoming this limitation. We demonstrate that even 5% bulk lubricant infusion in PDMS reduces viscoelastic dissipation in the substrate by more than 30 times and more than doubles the droplet nucleation density. We correlate the droplet nucleation and growth rate with the material properties controlled by design, i.e. the fraction and composition of uncrosslinked chains, shear modulus, and viscoelastic dissipation. Through in-situ, microscale condensation on the substrates, we show that the increase in nucleation density and reduction in pre-coalescence droplet growth rate is insensitive to the percentage of lubricant in PDMS. Our results indicate the presence of a lubricant layer on the substrate surface that cloaks the growing condensate droplets. We visualize the cloaking effect and show that lubricant infusion in PDMS significantly increases the rate of cloaking compared to PDMS without any lubricant infusion. Finally, we show that the overall enhanced condensation due to bulk lubricant infusion in PDMS leads to more than 40% increase in dewing on the substrate.

cond-mat.soft↗