Many of NASA's exploration scenarios include important roles for autonomous or partially autonomous robots. It is desirable for them to utilize human tools when possible, rather than needing to build custom tools for each robot. Control of robotic manipulators for tool usage generally requires a very precise Cartesian-space trajectory of the tool tip (e.g., moving a marker along the surface of a whiteboard or rotating a screwdriver about an axis). Well-known techniques exist for manipulator control in Cartesian space, most of which necessitate solving a series of Inverse Kinematics (IK) problems. Closed-form IK solvers work well for 7-degree-of-freedom (DOF) arms with rigid tool attachments, but cannot handle non-rigid tools that slip in the robot's hands. Numerical IK approaches are more generic and can handle non-rigid links to tools, but can be slow to converge. More importantly, if any joints fail or become limited in their range of motion, the robot arm essentially becomes 6-DOF or lower. IK solvers often fail in these lower DOF spaces because the configuration space becomes non-continuous and full of "holes". As a result, a 7-DOF robotic arm in space might be rendered largely useless if a single joint fails or even loses mobility until it can be serviced. TRACLabs proposes to investigate an alternative approach to traditional Cartesian control approaches, which rely on complex IK solvers that go from Cartesian space backwards to joint space. We propose to leverage cheap memory and modern processing speeds to instead perform simple computations that go from joint space forwards to Cartesian space. Such techniques should overcome common changes to a manipulation chain caused by tool slippage or the grasping of a new tool and to overcome uncommon changes to a chain caused by joint failures, reduced joint mobility, changes in joint geometry or range of motion, or added joints.