**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** OUR LONG-TERM GOAL IS TO DEVELOP A COMMERCIALLY VIABLE FULLY AUTONOMOUS PRUNING SYSTEM TO REDUCE DEPENDENCY ON SEASONAL SEMI-SKILLED WORKERS WHILE EXCELLING PRODUCTIVITY. THE OVERALL OBJECTIVE IS TO INVESTIGATE THE STATE-OF-THE-ART IN ROBOTICS TECHNOLOGY TO SIGNIFICANTLY IMPROVE AND STABILIZE THE BALANCE BETWEEN VEGETATIVE AND REPRODUCTIVE GROWTH THAT WOULD YIELD BETTER FRUIT QUALITY AND PREDICTABLE CROP LOAD. OUR APPROACH DEVIATES SIGNIFICANTLY FROM THE ESTABLISHED PARADIGM IN ROBOTIC GRAPEVINE PRUNING IN TWO MAJOR WAYS. FIRSTLY, IT IS RECOGNIZED THAT A GRAPEVINE TRAINING SYSTEM THAT FACILITATES ROBOTIC TECHNOLOGY IN VINEYARDS IS THE KEY TO THE SUCCESSFUL IMPLEMENTATION OF AUTONOMOUS AND SELECTIVE PRUNING OF VINES. SECOND, THE DESIGN OF THE PROPOSED ROBOT IS GENERAL-PURPOSE AND MULTI-FUNCTIONAL THAT MAKES IT COMPATIBLE WITH DIFFERENT VARIETIES AND CANOPY ARCHITECTURES AND IS MORE NOVEL COMPARED TO EXISTING SYSTEMS. FURTHERMORE, THE CONCEPT OF BALANCED PRUNING (BALANCING PLANT VEGETATIVE AND REPRODUCTIVE GROWTH) IS COMMON AMONG MOST WOODY PERENNIAL CROPPING SYSTEMS (APPLES, CHERRIES, AND OTHER TREE FRUITS, AND NUT TREES). THE TECHNOLOGY AND CONCEPTS DEVELOPED HERE FOR JUICE AND WINE GRAPES WOULD TRANSLATE TO OTHER SYSTEMS AS WELL.A FIRST ITERATION OF THE PROTOTYPE ROBOT THAT EMBODIES A SIMPLER AND COMMONLY PRACTICED SPUR PRUNING HAS ALREADY BEEN BUILT AND RECENTLY EVALUATED IN A COMMERCIAL VINEYARD. THIS EARLY STAGE SYSTEM EVALUATION PLAYED A CRUCIAL ROLE IN UNDERSTANDING THE PRACTICAL REQUIREMENTS IN THE FIELD. THE OBJECTIVES IN THIS PROPOSAL ARE SIGNIFICANT IMPROVEMENTS TO THE EXISTING SYSTEM AND ARE BASED ON THE \TEXTBF{LESSONS LEARNED} FROM USING THE PROTOTYPE IN REAL FIELD DEPLOYMENTS. WE ALSO USE DIVERSE VANGUARD LEARNING METHODOLOGIES IN SYNERGY WITH CLASSICAL APPROACHES TO PRUNE REAL VINES IN COMMERCIAL FIELDS. THUS, WE CAN OVERCOME THEIR INDIVIDUAL LIMITATIONS AND PUSH RESEARCH IN THE RIGHT DIRECTION AND LEVERAGES THE BENEFITS OF BOTH APPROACHES. WE BELIEVE THAT CONTINUITY OF THIS RESEARCH COULD LEAD TO A PRACTICAL AND ECONOMICAL SOLUTION FOR AUTOMATED PRUNING WITHIN A REASONABLE TIME FRAME. THE ADOPTION OF THIS TECHNOLOGY WILL HAVE SIGNIFICANT IMPACTS IN THE U.S. GRAPE INDUSTRY BOTH IN THE MID AND LONG TERMS.INTELLECTUAL MERITS: ROBOT SYSTEMS TO SELECTIVELY PRUNE GRAPE VINES DO NOT EXIST WHILE THE INDUSTRY HAS CLEAR NEEDS FOR IT IN TODAY'S ECONOMY. PRUNING A VINE WITHOUT ANY MODIFICATION AND IN ITS NATURAL FORM POSES MULTIPLE INTERESTING CHALLENGES THAT REQUIRES ADVANCED RESEARCH IN MULTIPLE BRANCHES OF ROBOTICS INCLUDING PERCEPTION, MANIPULATION, AND AI. IN THIS PROPOSAL WE INVESTIGATE FUNDAMENTAL RESEARCH ADVANCES IN ROBOTICS THAT WILL HAVE BROADER IMPACT, RANGING FROM AUTOMATION IN MORE GENERAL TREE CANOPIES TO A RANGE OF EVERYDAY TASKS THAT REQUIRE INTELLIGENT INTERACTION WITH FLEXIBLE MATERIALS IN CLUTTERED SPACES. FROM PERCEPTION PERSPECTIVE, WE PROPOSE ILLUMINATION-INVARIANT IMAGING CAPABILITIES TO GENERATE RELIABLE AND CONSISTENT PIXEL INFORMATION IN THE OUTDOOR ENVIRONMENT. DORMANT SEASON VINES CONTAIN DENSE, CRISS-CROSSING BRANCHES THAT EFFECTIVELY FILL A 3D VOLUME WHILE ALSO LEAVING MANY SMALL UNOCCUPIED SPACES. THE RESULTING HIGHLY OCCLUDED COMPLEX GEOMETRY IS DIFFICULT TO MODEL, AND EXISTING MODELING METHODS SUCH AS SLAM ARE NOT CAPABLE OF GENERATING COMPLETE MAPS. OUR APPROACH UNDER THE PERCEPTION GOAL ADDRESSES THIS COMPLEX PROBLEM WITH NOVEL APPROACH THAT SYSTEMATICALLY AND OPTIMALLY IDENTIFY REGION OF INTEREST(S) AND RECOVER MISSING INFORMATION TO COMPLETE VINE MODELS. SIMILARLY, DECIDING WHERE TO MAKE A PRUNING CUT REQUIRES INTELLIGENCE TO UNDERSTAND THE CANOPY AT MULTIPLE LEVELS, INCLUDING ITS GEOMETRY, ITS TOPOLOGY (WHAT IS CONNECTED TO WHAT), AND ITS SEMANTIC MEANING (WHAT PARTS ARE CANES, BUDS, ETC.). THE ABILITY TO AUTOMATICALLY GENERATE THIS LEVEL OF UNDERSTANDING DOES NOT CURRENTLY EXIST. THUS, FROM MANIPULATION STANDPOINT, WE ARE PUSHING RESEARCH BOUNDARIES TO OPERATE ROBOT ARMS IN CLUTTERED AND FULL OF FLEXIBLE OBJECTS AND AI MODULES THAT LEARNS TO AVOID OR PUSH AWAY OBJECTS IN ORDER TO REACH DEEPER INTO THE CANOPY. CURRENTLY EXISTING STANDARD MANIPULATION PLANNING APPROACHES ARE NOT EQUIPPED TO HANDLE THESE CASES.BROADER IMPACTS: (I) THIS RESEARCH WHILE PUSHING THE CURRENT BOUNDS OF ROBOTICS AND AI RESEARCH, ALSO HAS A REAL POTENTIAL TO DELIVER MORE PRODUCTIVE AND SUSTAINABLE AGRICULTURE, ESPECIALLY IN A SCENARIO OF FARMING LABOR SHORTAGE AND CLIMATE CHANGE. (II) THE APPROACHES PRESENTED HERE WOULD INCREASE THE ECONOMIC COMPETITIVENESS OF THE U.S. GRAPE INDUSTRY AND ESTABLISH A PARTNERSHIP BETWEEN ACADEMIA, INDUSTRY, AND STAKEHOLDERS. (III) THE RESEARCH TEAM WILL TRAIN UNDERREPRESENTED GROUPS, A GRADUATE STUDENT, AND EXPOSE LOCAL HIGH SCHOOL STUDENTS THROUGH ESTABLISHED PROGRAMS AT CMU.