9 research outputs found
Conformal Policy Learning for Sensorimotor Control Under Distribution Shifts
This paper focuses on the problem of detecting and reacting to changes in the
distribution of a sensorimotor controller's observables. The key idea is the
design of switching policies that can take conformal quantiles as input, which
we define as conformal policy learning, that allows robots to detect
distribution shifts with formal statistical guarantees. We show how to design
such policies by using conformal quantiles to switch between base policies with
different characteristics, e.g. safety or speed, or directly augmenting a
policy observation with a quantile and training it with reinforcement learning.
Theoretically, we show that such policies achieve the formal convergence
guarantees in finite time. In addition, we thoroughly evaluate their advantages
and limitations on two compelling use cases: simulated autonomous driving and
active perception with a physical quadruped. Empirical results demonstrate that
our approach outperforms five baselines. It is also the simplest of the
baseline strategies besides one ablation. Being easy to use, flexible, and with
formal guarantees, our work demonstrates how conformal prediction can be an
effective tool for sensorimotor learning under uncertainty.Comment: Conformal Policy Learnin
Language Embedded Radiance Fields for Zero-Shot Task-Oriented Grasping
Grasping objects by a specific part is often crucial for safety and for
executing downstream tasks. Yet, learning-based grasp planners lack this
behavior unless they are trained on specific object part data, making it a
significant challenge to scale object diversity. Instead, we propose LERF-TOGO,
Language Embedded Radiance Fields for Task-Oriented Grasping of Objects, which
uses vision-language models zero-shot to output a grasp distribution over an
object given a natural language query. To accomplish this, we first reconstruct
a LERF of the scene, which distills CLIP embeddings into a multi-scale 3D
language field queryable with text. However, LERF has no sense of objectness,
meaning its relevancy outputs often return incomplete activations over an
object which are insufficient for subsequent part queries. LERF-TOGO mitigates
this lack of spatial grouping by extracting a 3D object mask via DINO features
and then conditionally querying LERF on this mask to obtain a semantic
distribution over the object with which to rank grasps from an off-the-shelf
grasp planner. We evaluate LERF-TOGO's ability to grasp task-oriented object
parts on 31 different physical objects, and find it selects grasps on the
correct part in 81% of all trials and grasps successfully in 69%. See the
project website at: lerftogo.github.ioComment: See the project website at: lerftogo.github.i
Semantic Mechanical Search with Large Vision and Language Models
Moving objects to find a fully-occluded target object, known as mechanical
search, is a challenging problem in robotics. As objects are often organized
semantically, we conjecture that semantic information about object
relationships can facilitate mechanical search and reduce search time. Large
pretrained vision and language models (VLMs and LLMs) have shown promise in
generalizing to uncommon objects and previously unseen real-world environments.
In this work, we propose a novel framework called Semantic Mechanical Search
(SMS). SMS conducts scene understanding and generates a semantic occupancy
distribution explicitly using LLMs. Compared to methods that rely on visual
similarities offered by CLIP embeddings, SMS leverages the deep reasoning
capabilities of LLMs. Unlike prior work that uses VLMs and LLMs as end-to-end
planners, which may not integrate well with specialized geometric planners, SMS
can serve as a plug-in semantic module for downstream manipulation or
navigation policies. For mechanical search in closed-world settings such as
shelves, we compare with a geometric-based planner and show that SMS improves
mechanical search performance by 24% across the pharmacy, kitchen, and office
domains in simulation and 47.1% in physical experiments. For open-world real
environments, SMS can produce better semantic distributions compared to
CLIP-based methods, with the potential to be integrated with downstream
navigation policies to improve object navigation tasks. Code, data, videos, and
the appendix are available:
https://sites.google.com/view/semantic-mechanical-searc
Learning to Efficiently Plan Robust Frictional Multi-Object Grasps
We consider a decluttering problem where multiple rigid convex polygonal
objects rest in randomly placed positions and orientations on a planar surface
and must be efficiently transported to a packing box using both single and
multi-object grasps. Prior work considered frictionless multi-object grasping.
In this paper, we introduce friction to increase picks per hour. We train a
neural network using real examples to plan robust multi-object grasps. In
physical experiments, we find a 13.7% increase in success rate, a 1.6x increase
in picks per hour, and a 6.3x decrease in grasp planning time compared to prior
work on multi-object grasping. Compared to single object grasping, we find a
3.1x increase in picks per hour
Can Machines Garden? Systematically Comparing the AlphaGarden vs. Professional Horticulturalists
The AlphaGarden is an automated testbed for indoor polyculture farming which
combines a first-order plant simulator, a gantry robot, a seed planting
algorithm, plant phenotyping and tracking algorithms, irrigation sensors and
algorithms, and custom pruning tools and algorithms. In this paper, we
systematically compare the performance of the AlphaGarden to professional
horticulturalists on the staff of the UC Berkeley Oxford Tract Greenhouse. The
humans and the machine tend side-by-side polyculture gardens with the same seed
arrangement. We compare performance in terms of canopy coverage, plant
diversity, and water consumption. Results from two 60-day cycles suggest that
the automated AlphaGarden performs comparably to professional horticulturalists
in terms of coverage and diversity, and reduces water consumption by as much as
44%. Code, videos, and datasets are available at
https://sites.google.com/berkeley.edu/systematiccomparison.Comment: International Conference on Robotics and Automation(ICRA) 2023 Ora
Consumer Awareness and Adoption of One-Trip Travel Insurance: A Comparison among Generation X, Millennials and Generation Z
This research explores the awareness and adoption of one-trip insurance among different generational groups, namely Gen X, Gen Z, and Millennials. Utilizing a qualitative research design with purposive sampling and interview method of data collection, the study involved 24 participants, with eight individuals representing each generation. The findings of this study conclude that in-trip insurance is not a popular choice of security among most of the sample due to lack of awareness about the specific policy and insurance in general. Other barriers include cost, distrust in the system and utility of the insurance and false optimism.
The significance of the study arises from the observed lack of awareness and the presence of negligence and false optimism among individuals regarding insurance options.